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The MOVES® SLC™ is a portable computer controlled, electrically powered emergency ventilator intended to provide continuous or intermittent ventilatory support for the care of individuals who require mechanical ventilation.

a. Suction
The MOVES® SLC™ suction pump is intended for aspiration and removal of fluids, tissue (including bone), gases, bodily fluids or infectious materials from wounds or from a patient's airway or respiratory support system.

b. Supplementary Oxygen
The MOVES® SLC™ is intended to provide supplemental oxygen enriched air to patients that require supplemental oxygen.

c. Patient Monitoring
The MOVES® SLC™ is intended to monitor physiological parameters of patients and provide these parameters to a health care provider for interpretation in the form of physiological data and system alarms. Physiological data and system alarms will be available to the care provider from the monitor.

The MOVES® SLC™ (SLC) is an upgraded version of the cleared MOVES® SLC™ device (K140049), a portable multifunction patient support and monitoring system with the following capabilities:

• Computer controlled, electrically powered circle ventilator intended to provide continuous or intermittent ventilatory support for the care of individuals who require mechanical ventilation.
• Delivery of oxygen-enriched air that may be supplied from an external oxygen source or generated internal to the system with the on-board oxygen concentrator.
• Patient monitoring functions including the following patient parameters: Pulse Rate, Noninvasive BP (NIBP), Invasive BP (IBP), SPO2, Temperature, Respiration Rate, CO2, and O2.
• Suction/aspirator pump for medical suction procedures where secretions, blood and other body fluids must be removed through the application of continuous negative pressure.

The MOVES® SLCTM is capable of operating under battery power or external AC supply. It includes a handle and mounting equipment that allows it to attach to a stretcher.

The provided text is a 510(k) summary for the MOVES® SLC™ medical device, which is an emergency ventilator with additional functions. This document describes the device, its indications for use, and its comparison to predicate devices to demonstrate substantial equivalence. However, it does not include detailed acceptance criteria and a study proving the device meets those criteria in the format requested.

Specifically, the document:

• Does not provide a clear table of acceptance criteria and reported device performance. It offers a comparison table of features and characteristics between the MOVES® SLC™ and predicate devices, including some performance specifications like frequency range, tidal volume, and SPO2 accuracy, but these are comparative, not acceptance criteria.
• Does not mention anything about sample sizes used for test sets, data provenance, number of experts, adjudication methods, multi-reader multi-case (MRMC) studies, or standalone algorithm performance. This is because the device is a piece of hardware (ventilator, monitoring, suction, oxygen concentrator) and not an AI/software device that would typically involve such studies for regulatory clearance.
• Does not discuss a training set or how ground truth for a training set was established. This is irrelevant for a hardware medical device of this type.
• States that "Testing was conducted in accordance with all referenced standards and regulations, and to validate all system requirements" and a "Summary of Performance Testing" section mentions that "The results of performance testing demonstrate that the characteristics the MOVES® SLC™ are substantially equivalent to the identified predicates in terms of ventilator characteristics, patient monitoring performance, ability to delivery supplemental oxygen, and provide airway suction." However, it does not explicitly detail the specific performance test results against a defined set of acceptance criteria.

Therefore, I cannot extract the requested information as it is not present in the provided document. The document focuses on demonstrating substantial equivalence to existing predicate devices based on features, characteristics, and compliance with general medical device standards, rather than proving performance against specific acceptance criteria through clinical studies involving human readers or AI algorithms.

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The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on neonatal through adult patients. It should only be operated by properly trained clinical personnel, under the direction of a physician.

The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its graphical user interface module (UIM) has a flat panel color LCD with real time charting and digital monitoring capabilities, a touch screen for interaction, membrane kevs and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. Using internal batteries this provides inter-hospital transport as well as back up capability due to loss of AC power. The AVEA may be configured as a conventional ventilator or non-invasive positive pressure ventilator (NPPV). It has been designed to function using commonly available accessories.

The provided document is a 510(k) summary for the AVEA Ventilator, focusing on modifications for Volume Guarantee and Nasal Intermittent Positive Pressure Ventilation. It states that "Performance testing verified that the AVEA Ventilator meets its performance requirements and that this device is substantially equivalent to medical devices currently legally marketed in the United States." However, it does not provide specific details about the acceptance criteria or the study that proves the device meets those criteria, as requested in the prompt.

Therefore, I cannot provide a complete answer to your request based on the provided text. The document confirms that performance testing was done, but it omits the actual results and methodology of that testing.

Here's what I can extract and what is missing based on your request:

1. Table of acceptance criteria and the reported device performance:

• Missing from the document. The document states that performance testing "verified that the AVEA Ventilator meets its performance requirements," but it does not list these requirements (i.e., acceptance criteria) or the specific reported device performance against them.

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

• Missing from the document. The document mentions "Performance testing" but does not specify the sample size, data provenance, or whether it was retrospective or prospective.

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):

• Not applicable / Missing from the document. This type of information is typically relevant for AI/ML-based diagnostic devices where a "ground truth" needs to be established, often by human experts reviewing medical images or patient data. The AVEA Ventilator is a mechanical ventilator, and its performance testing would likely involve engineering and physiological measurements rather than expert review for "ground truth." Therefore, this information is not relevant in the context of this device or is completely absent.

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

• Not applicable / Missing from the document. Similar to point 3, adjudication methods are usually for resolving discrepancies in expert interpretations, which is not relevant for a mechanical ventilator's performance testing.

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:

• Not applicable / Missing from the document. An MRMC study is relevant for diagnostic AI tools involving human readers. This is not applicable to a mechanical ventilator like the AVEA Ventilator.

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

• Not applicable / Missing from the document. This question is also focused on AI/ML algorithms. The AVEA Ventilator has software that implements Volume Guarantee and Nasal Intermittent Mandatory Ventilation, which are automated functions. The document states "software implement Volume Guarantee which is the automated requlation of inspiratory pressure," implying standalone algorithmic function within the device. However, the performance study details for these automated functions are not provided.

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

• Not applicable / Missing from the document. As mentioned in point 3, the concept of "ground truth" in the context of expert review is unlikely to apply to the performance testing of a mechanical ventilator. The ground truth for such a device would typically be derived from engineering specifications, established physiological parameters, and direct measurement against those standards. The specific methods are not detailed.

8. The sample size for the training set:

• Not applicable / Missing from the document. A "training set" typically refers to data used to train an AI/ML model. While the ventilator has "software-driven" functions, the document does not suggest an AI/ML model that would require a distinct training set in the conventional sense. The "training" would be more akin to software development and verification/validation against specifications.

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

• Not applicable / Missing from the document. For the reasons stated in points 7 and 8, this information is not provided and likely not relevant in the AI/ML context.

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The AVEA Ventilator is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should be operated by properly trained clinical personnel, under the direction of a physician.

The AVEA Ventilator is a fourth generation servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its revolutionary user interface module (UIM) provides maximum flexibility wit6h simple operator interaction. It has a flat panel color LCD with real time graphic displays and digital monitoring capabilities, a touch screen for easy interaction, membrane keys and a dial for changing settings and operating parameters. A precision gas delivery engine with servo controlled active inhalation and exhalation improves performance over previous generations. The only design change is an optional Disposable Expiratory Filter / Water Trap to be used exclusively with the AVEA Ventilator.

The provided text is a 510(k) summary for the AVEA Ventilator's optional Disposable Expiratory Filter / Water Trap. It focuses on demonstrating substantial equivalence to predicate devices rather than proving a device performance claim through a clinical study with specific acceptance criteria.

The key points from the document regarding "acceptance criteria" and "study" are:

• Acceptance Criteria: The device is considered to meet acceptance criteria if it is "substantially equivalent in terms of BFE / VFE and resistance performance" to the predicate device (Clear-Guard II, K990949) and "has the same indicated use as the reusable existing filter / water trap" (integral to the AVEA Ventilator, K013642).
• Study: "Verification and Validation Testing demonstrated that the AVEA Disposable Expiratory Filter / Water Trap meets its performance requirements at both: Component Level and System Level, and, that this device is substantially equivalent to medical devices currently legally marketed in the United States."

This 510(k) summary does not provide the kind of detailed clinical study information (e.g., sample sizes, ground truth establishment, expert qualifications, MRMC studies) that would typically be associated with a device making a new performance claim or AI algorithm. It is a "substantial equivalence" review for a modification (an optional disposable filter) to an already cleared ventilator.

Therefore, the requested table and detailed information mostly cannot be extracted because the provided document does not contain that level of detail for this type of submission.

Here's a breakdown of what can be inferred or stated from the text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified. The document refers to "Verification and Validation Testing" but does not break down the specific sample sizes used for BFE/VFE or resistance testing.
• Data Provenance: Not specified. It's likely laboratory testing rather than patient data, given the nature of the filter.

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

• Not applicable as this is not a diagnostic device involving expert interpretation or ground truthing of clinical data in the traditional sense. Performance is based on physical and biological filtration efficiency measurements.

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

• Not applicable for the type of testing described (performance testing of a filter).

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, an MRMC study was not done. This device is a ventilator filter, not an AI-assisted diagnostic tool for human readers.

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

• No, this is a hardware device (filter), not an algorithm.

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

• The "ground truth" here is the measured performance metrics (BFE, VFE, resistance) against established engineering standards and the performance of the predicate device. It's not clinical "ground truth" derived from patient outcomes or expert consensus.

8. The sample size for the training set

• Not applicable. This is not an AI/machine learning device requiring a training set.

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

• Not applicable.

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