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The Leaf Patient Monitoring System monitors the orientation and activity of patients susceptible to pressure ulcers. It allows healthcare providers to implement individualized turn management plans and continuously monitor each patient. The Leaf Patient Monitoring System provides alerts when patient orientation or activity deviates from parameters set by healthcare providers. The device is intended for use in medical, nursing and long-term care facilities, including independent living, assisted-living and rehabilitation facilities.

The Leaf Patient Monitoring System is a medical device designed for use in hospitals, nursing homes, or other patient care facilities to monitor and report body orientation and activity, as well as to provide visual alerts for orientations and activity levels that fall outside of thresholds set by healthcare providers. The use of the Leaf Patient Monitoring System provides for continuous monitoring of patient position and allows caregivers to easily identify patients that are in need of caregiver-assisted turns according to the institution's guidelines or protocols. The use of the Leaf Patient Monitoring System can increase compliance with the care facility's prescribed patient tuning schedule and thereby may aid in the prevention of pressure ulcers.

The Leaf Patient Monitoring System is comprised of Patient Sensors, Leaf Antennas, and USB RF Transceivers, Turn Management Software, and a User Interface that can be viewed on a monitoring station. Each Leaf Patient Sensor is associated with a single patient, such that the patient's orientation, movements, and other care parameters can be monitored.

The provided text is a 510(k) premarket notification for the Leaf Patient Monitoring System. It focuses on demonstrating substantial equivalence to a predicate device (Centauri Medical, Inc. DynaSense System) rather than providing detailed acceptance criteria and a study proving the device meets those criteria, particularly in the context of an AI/algorithm-driven device with performance metrics like sensitivity, specificity, etc.

Based on the provided document, here's an attempt to answer the questions, highlighting where information is not present as per the request:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, specificity) for the Leaf Patient Monitoring System's performance in terms of monitoring patient orientation and activity, nor does it present specific reported performance metrics against such criteria. The focus is on demonstrating that the device "meets the established specifications necessary for consistent performance during its intended use" and "performs as intended."

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document mentions "System performance testing" as part of nonclinical bench testing but does not specify a sample size, test set, or data provenance (country of origin, retrospective/prospective). It does not describe a clinical study with patients to validate performance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is not provided as the document does not describe a clinical performance study involving expert-adjudicated ground truth.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

This information is not provided.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

An MRMC comparative effectiveness study was not mentioned or described. This device appears to be a monitoring system for patient orientation and activity, not an AI-assisted diagnostic imaging device that would typically involve human "readers." The system is designed to provide alerts and help caregivers with turn management, aiming to increase compliance with care facility protocols for pressure ulcer prevention.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The document describes "System performance testing" as part of nonclinical bench testing, implying standalone testing. However, the details of what this entailed (e.g., specific metrics for the algorithm's performance in detecting orientation changes) are not detailed. The device itself is described as a system that continuously monitors and communicates data wirelessly to a monitoring station that displays information via a user interface and provides alerts. Its purpose is to aid human caregivers rather than replace their decision-making entirely.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The document does not specify the type of ground truth used for its "System performance testing." Given it's a patient monitoring system for orientation and activity, ground truth would likely involve direct observation or independent measurement of patient position/movement, but this is not stated.

8. The sample size for the training set

The document does not mention a training set or its sample size. This suggests that while the device contains software, a deep learning or similar AI model requiring a large training set may not be the core technology being described for performance evaluation in this submission. The "Software verification" mentioned is more likely related to traditional software engineering validation.

9. How the ground truth for the training set was established

As no training set is mentioned, this information is not provided.

Summary of what the document focuses on:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Centauri Medical, Inc. DynaSense System) based on:

• Identical intended use and indications for use.
• Similar technological characteristics, with minor modifications (updated aesthetics, minor display changes, related software updates, and a non-adhesive frame around the sensor adhesive).
• Labeling changes, specifically the removal of a contraindication for pacemaker/ICD patients with the addition of an appropriate warning statement, which was analyzed not to raise new issues of safety or effectiveness.
• Nonclinical Testing Summary: This included "System performance testing," "Software verification," and "Electrical Safety and EMC." The collective results are stated to "demonstrate that the materials chosen, the manufacturing processes, and design... meet the established specifications necessary for consistent performance" and "do not raise new questions of safety or effectiveness."

Essentially, the submission leverages the predicate device's prior clearance to establish safety and effectiveness, affirming that the new device is functionally the same or improved without introducing new risks that would necessitate extensive new clinical performance studies with detailed acceptance criteria and ground truth validation for novel AI algorithms.

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QuietCare-Networked is intended for use in monitoring the environmental conditions and activity (motion) of an individual living in a senior housing community. QuietCare-Networked recognizes and monitors certain patterns of activity including but not limited to bathroom and bedroom activity, residence entry/exit, and interaction with food and medication storage.

Caregivers are provided with information and notification about the occurrence of, and changes in, these monitored activity patterns and environmental conditions. Noteworthy occurrences and changes are communicated to caregivers through direct notification (pager, voice alert, email) as well as a secure Internet website.

Data from QuietCare-Networked should not be relied on as medical advice or clinical diagnosis. Caregivers should always rely on licensed medical professionals in making all health decisions and should use the information provided by QuietCare as a resource in that process.

Caregivers should not rely solely on the use of QuietCare-Networked for care management of clients/residents. Caregivers should use standard care practices established within their care organization to ensure the safety and wellness of senior clients/residents.

Care Innovations QuietCare-Networked uses advanced motion sensors to monitor Activities of Daily Living for senior residents who require care assistance. It provides alerts and reporting information to care givers when conditions or trends are detected that indicate the senior resident may need care intervention.

The Intel-GE Care Innovations QuietCare-Networked device is a Class I Bed-Patient Monitor that uses motion sensors to monitor Activities of Daily Living for senior residents.

This submission explicitly states that clinical performance data was not used to demonstrate safety and efficacy. The device's equivalency was established by comparing its technological characteristics to predicate devices. Therefore, the information requested regarding acceptance criteria, study details, sample sizes, ground truth establishment, expert qualifications, and adjudication methods is not applicable to this particular 510(k) submission.

The device was deemed substantially equivalent based on similarities in software functionality, data collection methods, sensor types, communication methods, connectivity, communication protocol, and display method to existing commercially distributed predicate devices.

Here's a breakdown of why many of your excellent questions cannot be answered from the provided document:

• Acceptance Criteria & Reported Device Performance: Not provided as no clinical performance study was conducted.
• Sample Size (Test Set) & Data Provenance: Not applicable as there was no test set or clinical study.
• Number of Experts & Qualifications: Not applicable as there was no ground truth establishment by experts for a clinical study.
• Adjudication Method: Not applicable.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: No, this type of study was not conducted or presented.
• Standalone Performance (Algorithm Only): Not performed as a separate clinical study.
• Type of Ground Truth Used: Not applicable, as no clinical ground truth was established for performance evaluation.
• Sample Size for Training Set: Not applicable, as no training set for a clinical algorithm was mentioned.
• How Ground Truth for Training Set was Established: Not applicable.

Summary from the provided 510(k) Notification:

Study Details:

The 510(k) submission for the Intel-GE Care Innovations QuietCare-Networked did not rely on an assessment of clinical performance data to demonstrate safety and efficacy. Instead, substantial equivalence was claimed based on a comparison of technological characteristics with predicate devices.

Therefore, the following details are not applicable in this context:

• Sample size used for the test set and data provenance: N/A (no test set/clinical study performed).
• Number of experts used to establish the ground truth for the test set and their qualifications: N/A (no ground truth established for a clinical study).
• Adjudication method for the test set: N/A.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: No standalone performance study was described.
• The type of ground truth used: N/A (no clinical ground truth was established).
• The sample size for the training set: N/A (no training set for clinical performance was mentioned).
• How the ground truth for the training set was established: N/A.

The submission concluded that the device introduces no new questions concerning safety or efficacy because its technological characteristics (software functionality, data collection, sensor types, communication methods, connectivity, communication protocol, and display method) are substantially equivalent to the predicate devices.

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DynaSense monitors orientation and activity of patients susceptible to pressure ulcers. It allows healthcare providers to implement individualized turn management plans and continuously monitor each patient. DynaSense provides alerts when patient orientation or activity deviates from parameters set by healthcare providers. The device is intended for use in medical, nursing and long-term care facilities including independent living, assisted living and rehabilitation facilities.

DynaSense is a patient monitoring system that has been designed for use in hospitals, nursing homes, or other patient care facilities to aid standard care procedures for patients who are susceptible to pressure ulcers. The system monitors and reports patient activity and orientation as well as alerts the user (i.e., healthcare provider) when activity levels deviate from parameters set by healthcare providers. DynaSense is comprised of Patient Sensors, Relay Antennas, a USB RF Transceiver, Mesh Network Server Software, and User Interface software.

Each Patient Sensor is associated with a single patient, such that the patient's orientation and activity can be monitored. Data collected by the Patient Sensor is automatically communicated wirelessly to a nearby Relay Antenna, which subsequently relays these data to be displayed on the User Interface and maintained in a database. The system's Relay Antennas that are plugged into electrical outlets on the walls of the facility and the USB RF Transceiver that is plugged into the computer, on which the Mesh Network Server Software is installed or accessed, form a wireless network that allows data to be transmitted for display. The Mesh Network Server Software manages this network of Relay Antennas and USB RF Transceiver and collects the data from the Patient Sensors to allow monitoring of multiple patients on a single screen within the User Interface.

The Centauri Medical, Inc. DynaSense System, a bed-patient monitor, was reviewed for substantial equivalence (K130752). The device is intended to monitor patient orientation and activity to aid in pressure ulcer prevention.

Acceptance Criteria and Device Performance:

The provided document does not explicitly state quantitative acceptance criteria or a detailed table of device performance against such criteria. Instead, it broadly states that "the collective results of the testing demonstrate that the chosen materials, the manufacturing processes, and design of DynaSense meet the established specifications necessary for consistent performance during its intended use." It also concludes that the device "does not raise new questions of safety or effectiveness for monitoring patient activity when compared to the predicate devices."

The study described primarily focuses on qualitative assessments and established engineering standards to demonstrate substantial equivalence to predicate devices (Wireless MedCARE VivaTRAK™ System (K101109) and AFrame Digital MobileCare Monitor™ (K090138)).

Key Information from the Study:

1. Acceptance Criteria and Reported Device Performance:

   Acceptance Criteria Type	Reported Device Performance
   Design Verification (e.g., software verification)	The collective results of testing demonstrate that the design meets established specifications necessary for consistent performance.
   Electrical Safety Testing	The collective results of testing demonstrate that the device performs as intended in its intended use environment and does not raise new questions of safety or effectiveness.
   Electromagnetic Compatibility (EMC) Testing	The collective results of testing demonstrate that the device performs as intended in its intended use environment and does not raise new questions of safety or effectiveness.
   Safety and Effectiveness compared to Predicate Devices	"The collective testing results demonstrated that DynaSense does not raise new questions of safety or effectiveness for monitoring patient activity when compared to the predicate devices." Implies performance comparable to predicate devices in terms of patient activity monitoring, orientation tracking, and alert functionality to aid in pressure ulcer prevention. The device has "the same intended use and similar technological characteristics" as the predicate devices, with no differences raising new safety or effectiveness concerns. The device “performs as intended in its intended use environment.”

2. Sample size used for the test set and the data provenance:

   The document does not explicitly state the sample size of a test set, nor does it detail the specific data provenance (e.g., country of origin, retrospective/prospective). The performance testing mentioned is "design verification (e.g., software verification), Electrical Safety, and Electromagnetic Compatibility testing," which are typically conducted in a laboratory or controlled environment rather than with patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   This information is not provided in the document. The testing described focuses on engineering validation and regulatory compliance, not clinical performance assessed by experts against a ground truth in a clinical setting.

4. Adjudication method for the test set:

   This information is not provided. Given the nature of the testing (design, electrical, EMC), clinical adjudication methods would not typically apply.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   No MRMC comparative effectiveness study was done, as this is a patient monitoring device, not an imaging interpretation or diagnostic AI tool that would involve human "readers" or AI assistance for interpretation. The device itself provides alerts to healthcare providers based on set parameters.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   The device is a standalone monitoring system. It operates to detect patient orientation and activity and generate alerts based on predefined parameters. The "human-in-the-loop" component is where healthcare providers respond to these alerts and use the information to implement turn management plans. The core functionality of monitoring and alerting is performed by the algorithm/system autonomously.

7. The type of ground truth used:

   The document does not specify a "ground truth" in the clinical sense (e.g., pathology, outcomes data). The "ground truth" for the engineering tests would be the established specifications and accepted standards for electrical safety, EMC, and software functionality. For example, in electrical safety testing, the ground truth is adherence to voltage, current, and insulation limits.

8. The sample size for the training set:

   This information is not provided. The development of monitoring systems like DynaSense typically involves engineering design, calibration, and validation, rather than the "training set" concept common in machine learning or AI models developed from large datasets. While there is "Mesh Network Server Software" and "User Interface software," the description does not suggest a deep learning or similar AI model that would require a distinct "training set" for classification or prediction tasks.

9. How the ground truth for the training set was established:

   This information is not applicable, as no explicit "training set" in the context of machine learning/AI models is mentioned. Rather, the device's functionality is based on programmed logic and sensor data interpretation.

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The VivaTRAK™ system monitors in-bed activity and care delivery for patients susceptible to pressure ulcers and falls as well as those that require 24 hour monitoring of their general activity levels in medical, nursing and long-term care facilities including independent living, assisted living and rehabilitation facilities. The system providers information related to patient status and care delivered.

The VivaTRAK™ system is not intended to provide automated treatment decisions or used as a substitute for professional healthcare judgment. The VivaTRAK™ system is not a replacement or substitute for vital signs monitoring or alert equipment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate health care professional.

The VivaTRAK 110 system is used for monitoring in-bed patient activity and care delivery. The system monitors in-bed patient activity with the BedSense sensor, an under-the-mattress activity sensing pad, processing and wireless transmission of activity data with the ActivSense "M Bed Computer, and providing pager, email, phone and display notifications and care reports to the nursing staff, and then verifying using RFID readers that care was actually delivered with the VivaTRAKTM application. Care reports consisting of a notification, a RFID scan and bed activity are stored in a database and form the basis for reports used to improve quality of care and work flows at the facility.

The provided text is a 510(k) Pre-Market Notification Summary for the VivaTRAK™ system. This type of document is for regulatory clearance and focuses on demonstrating substantial equivalence to existing devices rather than a detailed performance study with acceptance criteria and statistical analysis as might be found in a clinical trial report for AI/ML devices.

Therefore, much of the requested information regarding acceptance criteria, sample sizes for test and training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for a specific device performance evaluation is not present in this document. The summary focuses on regulatory comparisons and claims of meeting functional specifications rather than presenting detailed study results in the manner an AI/ML study would.

Here's a breakdown of what can be extracted and what information is missing:

Acceptance Criteria and Reported Device Performance

The document states: "Wireless MedCARE has verified and validated that the VivaTRAK™ system meets its functional, performance, safety, and efficacy specifications and requirements." However, it does not disclose what these specific functional, performance, safety, and efficacy specifications (i.e., acceptance criteria) are, nor does it provide a table of performance metrics to demonstrate meeting those criteria.

Table of Acceptance Criteria and Reported Device Performance (Information Not Provided):

Study Information (Based on Available Text)

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not specified.
   • Data Provenance: Not specified. The document states a "non-clinical performance summary" without detailing the type of data (e.g., patient data, simulated data) or its origin.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable/Not specified. The document does not describe a study involving expert-established ground truth for performance evaluation in the context of diagnostic accuracy. The device monitors in-bed activity and care delivery, which is likely validated through direct observation or automated logging, rather than expert interpretation of images or signals.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not applicable/Not specified. This is typically relevant for studies where human disagreement needs to be resolved for ground truth or performance assessment, which is not described for this device.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No. An MRMC study was not described or implied. The VivaTRAK™ system is described as providing information to nursing staff and verifying care delivery, not as an AI/ML diagnostic aid that human readers would interpret or use to improve performance.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • The document implies that the system operates autonomously in monitoring activities and generating reports and notifications. It states, "The VivaTRAK™ system monitors in-bed patient activity... processing and wireless transmission of activity data... and providing pager, email, phone and display notifications and care reports to the nursing staff, and then verifying using RFID readers that care was actually delivered..." This suggests a standalone functional operation. However, no specific "standalone performance study" with detailed results (e.g., accuracy of activity detection, notification timeliness) is provided. Instead, it broadly states "meets its functional, performance, safety, and efficacy specifications."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Not explicitly stated. Given the device's function (monitoring in-bed activity and care delivery), the "ground truth" would likely be derived from direct observation of patient activity, direct logging of care delivery interactions (via RFID scans), or other objective measurements, rather than expert consensus on diagnostic images or pathology.

7. The sample size for the training set:

   • Not applicable/Not specified. While the system "processes" data, the document does not suggest it uses machine learning models that require a distinct "training set" in the common sense of AI/ML development. It's a sensor-based monitoring system.

8. How the ground truth for the training set was established:

   • Not applicable/Not specified, as no training set for an AI/ML model is described.

Summary of Device and Performance Claims from Document:

The VivaTRAK™ system is a "Monitor, Bed Patient" that:

• Monitors in-bed patient activity with an under-the-mattress sensor (BedSense).
• Processes and wirelessly transmits activity data with an "ActivSense™ Bed Computer."
• Provides pager, email, phone, and display notifications and care reports to nursing staff.
• Verifies care delivery using RFID readers and the VivaTRAK™ application.
• Stores care reports for quality improvement and workflow analysis.

The claims regarding performance are general: "Wireless MedCARE has verified and validated that the VivaTRAK™ system meets its functional, performance, safety, and efficacy specifications and requirements." The 510(k) clearance is primarily based on demonstrating substantial equivalence to predicate devices (AFrame Digital, Inc.'s AFrame MobileCare Monitor™, Stanley Security Solution's TAS Stilite, and Emfit Ltd's SafeBed) rather than presenting novel performance metrics from a detailed clinical/technical study with specific acceptance criteria.

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The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The provided 510(k) summary for the KOGO 138 MobileCare Monitor™ does not contain information about specific acceptance criteria related to a clinical study or device performance metrics like sensitivity, specificity, or accuracy. It focuses on demonstrating substantial equivalence to predicate devices primarily through comparison of technical characteristics and intended use, and conformance to non-clinical safety (FCC regulations) and software validation standards.

Therefore, the following information cannot be extracted from the provided text:

• A table of acceptance criteria and the reported device performance: No specific performance metrics or acceptance criteria are stated. The document indicates software validation was performed, but no results are provided.
• Sample size used for the test set and the data provenance: Not applicable as no clinical test set is described.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• Adjudication method for the test set: Not applicable.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device is a monitor with alerting capabilities; performance is not typically described in terms of "algorithm only" in this context. The document mentions an "impact sensor that may indicate a fall" and an "emergency (panic) button."
• The type of ground truth used: Not applicable as no clinical performance study is detailed with ground truth.
• The sample size for the training set: Not applicable as a machine learning training set is not mentioned for this device type.
• How the ground truth for the training set was established: Not applicable.

Here's a summary of the available information regarding acceptance criteria and studies:

1. Acceptance Criteria and Reported Device Performance:

The document primarily relies on demonstrating substantial equivalence to predicate devices and adherence to non-clinical standards rather than clinical performance metrics.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified, as no clinical test set for performance evaluation is described in the provided text. The evaluation focuses on non-clinical aspects and substantial equivalence.
• Data Provenance: Not applicable, as no clinical performance data are presented.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable, as no clinical test set requiring expert-established ground truth is described.

4. Adjudication method for the test set:

• Not applicable, as no clinical test set requiring adjudication is described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No, a multi-reader multi-case comparative effectiveness study was not done or reported. This type of study is more common for diagnostic imaging AI devices, whereas the MobileCare Monitor™ is a monitoring and alerting system.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• The document does not describe a "standalone" algorithmic performance study in the context typically used for AI/ML devices (e.g., measuring accuracy of an image analysis algorithm). The device itself functions as a standalone monitoring system that provides alerts.

7. The type of ground truth used:

• Not applicable, as no clinical performance study with defined ground truth is described. The "performance" aspect refers to software validation.

8. The sample size for the training set:

• Not applicable, as no machine learning algorithm requiring a training set is explicitly mentioned or detailed in the provided information. The device functions based on sensors and pre-set thresholds, not a learned model.

9. How the ground truth for the training set was established:

• Not applicable, as no machine learning algorithm or training set is described.

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