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The OrthoScan TAU Mini C-arm is designed to provide physicians with general fluoroscopic visualization, using pulsed or continuous fluoroscopy, of a patient including but not limited to, diagnostic, surgical, and critical emergency care procedures for patients of all ages including pediatic populations when imaging limbs/extremittes, shoulders; at locations including but not limited to, hospitals, ambulatory surgery, emergency, traumatology, orthopedic, critical care, or physician office environments.

The OrthoScan TAU Mini C-Arm is a mobile fluoroscopic mini Carm system that provides fluoroscopic images of patients of all ages during diagnostic, treatment and surgical procedures involving anatomical regions such as but not limited to that of extremities, limbs, shoulders, knees, and Hips. The system consists of C-arm support attached to the image workstation. The proposed device provides the option of three CMOS flat panel detector sizes and identical X-ray source HVPS monoblock generator assembly with continuous or pulsed operation for image acquisition. The C-arm supports the CMOS FPD, X-Ray controls, collimator, high voltage generator with a fixed SID imaging. The C-arm and support arm which is connected to the mobile workstation platform are mechanically balanced allowing the operator precise positioning and locking of the vertical, horizontal, orbital and rotational movements at various angles and distances when imaging the patient's anatomical structures. The main workstation platform that supports the C-arm assembly contains the power control system, image processing system, system software, monitor display control and main user interface controls. The combination of C-Arm and workstation provides the clinician with a stable platform to obtain precise angles for localizing the patient's anatomical structures and visualization of pathology during live fluoroscopic imaging. The touch screen interface and keyboard provide user concise selectable imaging, X-ray technique control, entry of patient demographics and related procedural information. The workstation supports both an optional wired or wireless fluoroscopic footswitch allowing optimal positioning for the clinician. The optional connector interface panel of the OrthoScan TAU Mini C-Arm provides convenient connection of peripheral devices such as thermal video printers, image storage devices (USB) and DICOM fixed wire and wireless network interfaces.

The provided text is a 510(k) Summary for the OrthoScan TAU Mini C-Arm, which is a premarket notification to the FDA to demonstrate that the new device is substantially equivalent to a legally marketed predicate device. This type of submission focuses on non-clinical testing to support the claim of substantial equivalence, rather than a full clinical study with specific acceptance criteria and performance metrics typically seen for novel devices or AI/software as a medical device (SaMD) where performance improvement is a key claim.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes for test/training sets, expert qualifications, and ground truth establishment, which are standard for AI/SaMD studies, are not explicitly provided in this document as it pertains to a traditional medical imaging device (C-arm) that primarily demonstrates substantial equivalence to existing technology.

However, I can extract the information that is present regarding device performance and the "study" conducted to support substantial equivalence.

Here's a breakdown of what can be inferred or directly stated from the document, and what is missing due to the nature of this 510(k) submission for an imaging device, not an AI algorithm:

Acceptance Criteria and Reported Device Performance

The document doesn't present a table of "acceptance criteria" in the traditional sense of specific numerical thresholds for diagnostic performance (e.g., sensitivity, specificity, AUC) that an AI algorithm would be tested against. Instead, it aims to demonstrate "substantial equivalence" to a predicate device. The performance is assessed through "image comparison" and "dose assessment" to show that the new device performs "as intended" and provides "similar image quality with new IDR filter" at "lower entrance dose level" compared to the predicate.

The table below summarizes the comparative technological characteristics which are used to argue substantial equivalence, and indirectly imply performance. The primary "performance" studied here is image quality and dose reduction, not diagnostic accuracy of an AI.

Table of Performance Comparison (Excerpted and Reinterpreted from the Provided Document)

Study Details (as inferable from the 510(k) Summary)

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Images: 330 individual images arranged in 20 groups of image sets.
   • Data Provenance: The study involved images taken from "anthropomorphic (PMMA material) phantoms and anatomical simulation phantoms." This means the data is synthetic/phantom-based, not from human patients. The country of origin is not specified, but given the FDA submission, it's presumed to be a controlled laboratory setting. The study is inherently non-clinical (not retrospective or prospective on human subjects).

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • Number of Experts: 1 Radiologist.
   • Qualifications: "a board-certified Radiologist." No specific years of experience or sub-specialty are explicitly mentioned beyond board certification.

3. Adjudication Method for the Test Set:

   • There is no mention of an adjudication method (like 2+1 or 3+1). The evaluation was "conducted by a board-certified Radiologist." This implies a single reader assessment for comparison.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No. The document explicitly states: "OrthoScan TAU mobile fluoroscopic mini C-arm system did not require live human clinical studies to support substantial equivalence... Therefore, OrthoScan conducted a lab test image comparison study employing the use of anthropomorphic phantoms..." The study was a "lab test image comparison study" and involved a "Radiologist performed an assessment of 330 individual images." This is not an MRMC study.
   • Effect Size of Human Readers Improve with AI vs. without AI assistance: Not applicable, as this is a device clearance, not an AI algorithm. The device aims to provide better image quality at lower dose, which indirectly can improve human interpretation, but this was not quantified in an MRMC study.

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

   • Not applicable as this is a medical imaging device (C-arm), not an AI algorithm. The "performance" being evaluated is the direct image output of the device itself and its dose characteristics, not a diagnostic output from an automated algorithm.

6. The Type of Ground Truth Used:

   • Phantom-based comparison with expert assessment. The "ground truth" for image quality and dose reduction in this context is established by the comparative assessment of images generated using standardized phantoms and evaluated by a qualified radiologist in conjunction with laboratory performance data (e.g., on dose). There's no "pathology" or "outcomes data" ground truth as this is a technical assessment of an imaging device.

7. The Sample Size for the Training Set:

   • Not applicable. This is a medical device, not an AI/machine learning algorithm, so there is no "training set" in the computational sense. The device's design and software are developed through engineering and quality processes, not through autonomous learning from a dataset.

8. How the Ground Truth for the Training Set was Established:

   • Not applicable due to the reasons stated above.

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The Cios Spin is a mobile X-Ray system designed to provide X-ray imaging of the anatomical structures of patient during clinical applications. Clinical applications may include but are not limited to: interventional fluoroscopic, gastro- intestinal, endoscopic, urologic, pain management, orthopedic, neurologic, vascular, cardiac, critical care and emergency room procedures. The patient population may include pediatric patients.

The Cios Spin mobile fluoroscopic C-arm X-ray System designed for the surgical environment. The Cios Spin provides comprehensive image acquisition modes to support orthopedic and vascular procedures. The system consists of two major components:
a) The C-arm with X-ray source on one side and the flat panel detector on the opposite side. The c-arm can be angulated in both planes and be lifted vertically, shifted to the side and move forward/backward by an operator.
b) The second unit is the image display station with a moveable trolley for the image processing and storage system, image display and documentation. Both units are connected to each other with a cable.

This document describes the premarket notification (510(k)) for the Siemens Cios Spin X-ray system. The information provided is primarily focused on demonstrating substantial equivalence to predicate devices, rather than a standalone clinical study with detailed acceptance criteria for a new AI feature.

However, based on the provided text, we can infer acceptance criteria and the studies performed for specific features, particularly those listed under "New Software VA30 due to new functionality."

Summary of Device and Context:
The Cios Spin is a mobile fluoroscopic C-arm X-ray system for imaging anatomical structures during various clinical applications, including interventional, orthopedic, and neurological procedures. The 510(k) submission highlights several modifications and new software functionalities compared to its predicate device, the Cios Alpha.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a dedicated section for the new software features. Instead, it relies on comparative equivalence and verification/validation testing against established guidance and predicate device performance. For the detector, quantitative metrics are provided.

• Conformance to FDA Recognized Consensus Standards and Guidance Documents.
• Software specifications meet acceptance criteria.
• Risk analysis completed and controls implemented for identified hazards.
• Safe and effective for intended users, uses, and environments (through design control V&V). | - Complies with 21 CFR 1020.30, 1020.32.
• Certified to comply with AAMI ANSI ES60601-1:2005/(R)2012, IEC 60601-1-2:2014, IEC 60601-1-3:2013, IEC 60601-1-6:2010/A1:2013, IEC 60825-1:2014, IEC 62304:2015, IEC 60601-2-28:2010, IEC 60601-2-43:2017, IEC 60601-2-54:2009/A1:2015, ISO 14971:2007, IEC 62366-1:2015/Cor1:2016.
• Verification and validation testing found acceptable, supporting claims of substantial equivalence.
• All new software functions validated; worked as intended.
• Human Factor Usability Validation showed human factors addressed, with adequate training for employees.
• Cybersecurity statement provided, considering IEC 80001-1:2010. |
  | New Software (e.g., Metal Artifact Reduction, Retina 3D, Screw Scout, Target Pointer, High Power 3D, Easy 3D) | - Does not raise any new issues of safety or effectiveness.
• Works as intended (for new software functions). | - For Metal Artifact Reduction: The algorithm is unchanged from a previously cleared device (syngo Application Software VD20B, K170747). Improves image quality by reducing artifacts.
• For Retina 3D, Screw Scout, Target Pointer, Cios Open Apps: Non-clinical testing and Software Verification/Validation testing conducted and acceptable per Software Guidance document. Retina 3D has the same reconstruction algorithm as predicate ARTIS Pheno.
• For High Power 3D & Easy 3D: Does not raise any new issues of safety or effectiveness per Software Guidance. |
  | CMOS Flat Panel Detector | - Equivalent image quality to a-Si technology detector.
• Does not raise any new issues of safety or effectiveness.
• Compliance with "Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices" for performance metrics. | - DQE: 72% (vs. Predicate Cios Alpha 76%, Reference Ziehm Solo FD 70%)
• Dynamic Range: 96dB (vs. Predicate Cios Alpha 94dB, Reference Ziehm Solo FD Equivalent)
• MTF: 58% at 1 lp/mm (large) (vs. Predicate Cios Alpha 55% at 1 Lp/mm, Reference Ziehm Solo FD 4lp/mm)
• Digitization Depth: 16 bit (same as predicates/references)
• Pixel Pitch: 152 μm (vs. Predicate Cios Alpha 194μm, Reference Ziehm Solo FD 100 μm)
• Field of View: 30 cm x 30 cm; 20 cm x 20 cm |

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a "test set" in terms of patient data for evaluating the new software features. The testing mentioned is primarily non-clinical performance testing, software verification and validation (V&V), human factors usability validation, and engineering bench testing.

• Sample Size: Not applicable in the context of patient data for the new software features, as the testing described is primarily technical and comparative against existing standards and predicate devices. For the detector, the metrics (DQE, MTF, etc.) are derived from laboratory measurements, not patient data sets.
• Data Provenance: Not specified as patient data is not the primary focus for the equivalence argument. The testing was conducted by Siemens Healthcare GmbH Corporate Testing Laboratory (for conformance standards) and internally for software V&V. This implies internal company testing, likely in a controlled laboratory environment.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

Given that the testing is primarily non-clinical and focused on technical performance and software functionality, the concept of "ground truth established by experts" for a patient-based test set is not directly applicable in the way it would be for an AI diagnostic algorithm.

• Experts: The "experts" involved are implied to be the engineers and technical specialists responsible for conducting the non-clinical tests, software verification/validation, and human factors evaluations. The approval by the FDA also involves review by regulatory experts.
• Qualifications: While not explicitly stated, these would be Siemens' internal development and quality assurance teams, as well as external certification bodies for standards compliance.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a patient-based test set requiring expert adjudication for ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done for the Cios Spin in this 510(k) submission. The submission focuses on demonstrating substantial equivalence through technological characteristics, non-clinical performance data, and software validation. It does not include an assessment of how human readers improve with or without AI assistance.

6. Standalone (Algorithm Only Without Human-In-The-Loop Performance) Study

The document does not describe a standalone performance study for the software features (e.g., Metal Artifact Reduction, Retina 3D, Screw Scout, Target Pointer) similar to what would be done for a diagnostic AI algorithm. Instead, it states that "All new software functions present in the Subject Device... have been validated through detailed software testing and it was founded they worked as intended." This implies internal functional and performance testing, but not a standalone clinical performance study typically associated with AI algorithms.

7. Type of Ground Truth Used

The "ground truth" for the various new features is established through:

• Engineering specifications and design requirements: For software functionality and hardware performance.
• Compliance with recognized industry standards: (e.g., IEC standards, FDA performance standards)
• Comparison to predicate devices and reference devices: For performance metrics (e.g., DQE, MTF for the detector), where "equivalent" or "comparable" performance serves as the ground truth.
• Expected "working as intended" functionality: For the new software features validated through detailed software testing.

There is no mention of pathology, expert consensus on patient cases, or outcomes data used to establish ground truth for the specific performance of these new features in a clinical setting.

8. Sample Size for the Training Set

Not applicable. The document describes a medical device (X-ray system) with new software features, not a machine learning model that requires a "training set" in the context of AI/ML development. The software validation is based on internal testing against specifications.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as there is no "training set" as understood in machine learning. The "ground truth" for the software validation mentioned in the document is based on meeting pre-defined software specifications and functional requirements through verification and validation testing.

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The Cios Alpha is a mobile X-Ray system designed to provide X-ray imaging of the anatomical structures of patient during clinical applications. Clinical applications may include but are not limited to: interventional fluoroscopic, gastro- intestinal, endoscopic, urologic, pain management, orthopedic, neurologic, vascular, cardiac, critical care and emergency room procedures. The patient population may include pediatric patients.

The Cios Alpha mobile fluoroscopic C-arm X-ray System is designed for the surgical environment. The Cios Alpha provides comprehensive image acquisition modes. The system consists of two major components:
a) The C-arm with an X-ray source on one side and the flat panel detector on the opposite side. The c-arm can be angulated in both planes and lifted vertically, shifted to the side and moved forward/backward by an operator.
b) The second component is the image display station with a moveable trolley that holds the image processing and storage system, and the image display. Both components are connected to each other with a cable.

Here's an analysis of the acceptance criteria and study information provided in the document for the Cios Alpha (VA30) device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for quantitative performance metrics in a pass/fail format. Instead, it presents a comparison of the Subject Device's (Cios Alpha (VA30)) performance to its Predicate (Cios Alpha (VA10)) and Reference Devices for Solid State X-Ray Imaging (SSXI) specifications. The implication is that comparable or better performance is the acceptance criterion for the SSXI metrics.

Additional Acceptance Criteria (General):

• Compliance with voluntary standards (Table 3), FDA Guidance Documents (Table 4).
• Software verification and validation meeting acceptance criteria.
• Risk analysis completed and hazards mitigated.
• Human Factors Usability Validation showing addressing human factors and successful clinical use tests.
• Cybersecurity requirements met.

Study Proving Device Meets Acceptance Criteria:

The document describes several non-clinical performance tests and analyses to demonstrate that the Cios Alpha (VA30) meets the acceptance criteria, primarily for substantial equivalence to its predicate devices.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: The document does not specify a distinct "test set" sample size in terms of patient data or number of images for evaluating the SSXI metrics. The performance evaluation seems to be based on engineering bench testing of device capabilities rather than a separate clinical image set.
• Data Provenance: The data provenance for the SSXI metrics and other performance tests is non-clinical bench testing. The document states: "Performance tests were conduct[ed] to test the functionality of the Cios Alpha (VA30)." It also mentions "Additional engineering bench testing was performed including: the non-clinical testing identified in the guidance for submission of 510(k) s for Solid State X-Ray Imaging Devices (SSXI); demonstration of system performance; and an imaging performance evaluation."
  • The "clinical images are not required" statement further confirms the non-clinical nature of the specific SSXI evaluation.
  • The Human Factor Usability Validation mentions "clinical use tests with customer report and feedback form," which implies some level of prospective, real-world (or simulated real-world) interaction, but specific sample sizes are not provided.
  • The origin of the data is Siemens Healthcare GmbH Corporate Testing Laboratory and internal verification/validation processes.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• The document does not describe the use of experts to establish ground truth for a test set in the traditional sense of image interpretation for diagnostic accuracy. The testing primarily focuses on technical specifications of the imaging system itself.
• For the Human Factors Usability Validation, "customer report and feedback form" are mentioned, implying input from users (healthcare professionals), but no specific number or detailed qualifications are provided.

4. Adjudication Method for the Test Set

• Given that the primary performance evaluation described is non-clinical bench testing of engineering specifications (SSXI metrics), an adjudication method for a test set based on expert consensus would not be applicable or mentioned. The "ground truth" for these metrics is objectively measured device performance.

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 is mentioned. This submission is for an imaging system (C-arm X-ray system), not an AI-powered diagnostic algorithm that assists human readers. While it includes "new software functions" like "Target Pointer," which "enables the automatic detection of K-wires and displays the trajectory," the document does not present a study evaluating the impact of this feature on human reader performance or diagnostic accuracy.

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

• The document evaluates the Cios Alpha as an imaging system, not a standalone AI algorithm. While it contains new software features, the performance metrics discussed (e.g., DQE, MTF) are system-level imaging characteristics. The "Target Pointer" feature performs automatic detection, but its standalone performance (e.g., accuracy of K-wire detection) is not detailed in the provided text. The overall context is regulatory clearance for hardware and software modifications of an existing medical device, not a new AI-enabled diagnostic device undergoing standalone performance evaluation.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• For the SSXI performance metrics (DQE, Dynamic Range, MTF, etc.), the "ground truth" is based on objective physical measurements and technical standards. These are inherent properties of the imaging system's detector and processing.
• For software functions, "ground truth" is established through detailed software testing to confirm they "worked as intended" according to specifications and requirements.
• For Human Factors, ground truth would relate to usability and safety observations and feedback during "clinical use tests."

8. The Sample Size for the Training Set

• The document does not mention a training set sample size. This is expected as the submission primarily concerns an imaging system rather than a machine learning algorithm requiring a distinct training phase with annotated data. Although new software features are present, the submission focuses on their validation as part of the overall device.

9. How the Ground Truth for the Training Set Was Established

• Since no training set is discussed, the method for establishing its ground truth is also not applicable in this document.

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The OEC Elite mobile fluoroscopy system is designed to provide fluoroscopic and digital spot images of adult and pediatric populations during diagnostic, interventional and surgical procedures. Examples of a clinical application may include: orthopedic, gastrointestinal, endoscopic, urologic, critical care and emergency procedures.

The OEC Elite is a Mobile Fluoroscopic C-arm Imaging system used to assist trained surgeons and other qualified physicians. The system is used to provide fluoroscopic X-Ray images during diagnostic, interventional, and surgical procedures. These images help the physician visualize the patient's anatomy and interventional tools. This visualization helps to localize clinical regions of interest and pathology. The images provide real-time visualization and records of pre-procedure anatomy, in vivo-clinical activity and post-procedure outcomes. The system is composed of two primary physical components. The first is referred to as the "C - Arm" because of its "C" shaped image gantry; the second is referred to as the "Workstation", which is the primary interface for the user to interact with the system.

The C-arm is a stable mobile platform capable of performing linear motions (vertical, horizontal) and rotational motions (orbital, lateral, wig-wag) that allow the user to position the X-Ray image chain at various angles and distances with respect to the patient anatomy to be imaged. The C - arm is mechanically balanced allowing for ease of movement and capable of being "locked" in place using a manually activated lock. The C-Arm is comprised of the high voltage generator, software, X-ray control, and a "C" shaped image gantry, which supports an X-ray tube and a Flat Panel Detector or Image Intensifier, depending on the choice of detector configuration desired.

The workstation is a stable mobile platform with an articulating arm supporting a color image, high resolution, LCD display monitor. It also includes image processing equipment/software, recording devices, data input/output devices and power control systems.

The primary purpose of the mobile fluoroscopy system is to provide fluoroscopic images of the patient during diagnostic, interventional, and surgical procedures such as orthopedic, gastrointestinal, endoscopic, urologic, neurologic, critical care and emergency procedures.

The OEC Elite comes with four image receptor (detector) options: the choice of 21x21 cm or 31x31 cm (new) Thallium-doped Cesium Iodide Cs] solid state flat panel X-ray detector with Complementary Metal Oxide Semiconductor (CMOS) light imager; or the choice of a 9 inch or 12 inch of the same existing image intensifier as in the OEC 9900 Elite.

This document describes the OEC Elite, a mobile fluoroscopy system. It does not contain information on human performance studies or the establishment of ground truth by expert consensus for evaluating clinical tasks. Instead, it focuses on non-clinical engineering and imaging performance testing against defined metrics.

Here is an analysis based on the provided text, focusing on the acceptance criteria and the study proving the device meets them:

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

The document outlines acceptance criteria based on performance metrics for Solid State X-ray Imaging Devices (SSXI) and compares the OEC Elite to its predicate device, OEC 9900 Elite.

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

The document refers to "additional engineering bench testing" and "imaging performance evaluation using anthropomorphic phantoms." It does not specify a distinct "test set" in terms of clinical images or patient data, nor does it provide a sample size for such a set. It appears the performance evaluations were conducted on the device itself and phantoms.

The data provenance is through non-clinical testing using anthropomorphic phantoms in a laboratory setting at GE OEC Medical Systems, Inc. (GE Healthcare).

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

This information is not applicable as the evaluation was based on non-clinical engineering and imaging performance metrics, primarily using anthropomorphic phantoms and objective measurements. There was no mention of human experts establishing ground truth for a clinical test set in this context.

4. Adjudication method for the test set:

This information is not applicable as there was no clinical test set requiring human adjudication to establish ground truth. The evaluation focused on technical performance metrics against a predicate device.

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. The device described (OEC Elite) is a mobile fluoroscopy system, a medical imaging hardware device, not an AI-powered diagnostic tool that assists human readers. Therefore, the concept of "human readers improve with AI vs without AI assistance" does not apply to this submission.

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

This is not applicable in the context of an x-ray imaging system. The OEC Elite is a hardware device that produces images, not an algorithm, and the performance criteria relate to image quality and system functionality, not algorithmic output without human intervention.

7. The type of ground truth used:

The "ground truth" for the non-clinical testing was established by objective measurements of physical performance metrics (e.g., DQE, spatial resolution) on the OEC Elite and compared to the established performance of the predicate device (OEC 9900 Elite) and reference devices. Additionally, compliance with recognized standards (e.g., IEC 60601-1 Ed. 3 series, 21CFR Subchapter J) served as a form of "ground truth" for safety and efficacy.

8. The sample size for the training set:

This information is not applicable. The OEC Elite is a medical imaging hardware system, not a machine learning or AI algorithm that requires a "training set."

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

This information is not applicable as there was no training set.

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