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The Deployable Oxygen Generator System - Small (DOGS-S) is intended for the administration of supplemental oxygen. This device is not intended for life support nor does it provide any patient monitoring capabilities. The system will be operated by trained personnel.

The DOGS-S system is a portable battery powered oxygen concentrator that will be operationally used for ground-based medical assemblages, such as the Expeditionary Medical Support (EMEDS), and En-Route Patient Staging System (ERPSS) in deployed scenarios, including wartime operations, deterrence, humanitarian and contingency operations.

The provided text describes a 510(k) premarket notification for a medical device called the "Deployable Oxygen Generator System - Small (DOGS-S)". This document focuses on demonstrating substantial equivalence to a predicate device (OMNI 2 (SAROS)), not on proving the device meets individual acceptance criteria through a clinical study in the way an AI/ML device would be evaluated for diagnostic accuracy.

Therefore, many of the requested criteria (e.g., sample size for test set, number of experts for ground truth, MRMC study, training set details) are not applicable to this type of regulatory submission and are not present in the document.

The document primarily focuses on bench testing to confirm the device's technical specifications and compliance with relevant engineering and safety standards.

Here's an analysis based on the information available in the provided text:

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

The document doesn't present "acceptance criteria" in the typical format of a clinical performance study with specific metrics and thresholds (e.g., sensitivity, specificity). Instead, it provides a comparison table of technological characteristics between the DOGS-S device and its predicate, the OMNI 2 (SAROS), to demonstrate substantial equivalence. The "acceptance" here is that the DOGS-S performs comparably or meets the standards for an oxygen concentrator.

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

• Sample Size: Not explicitly stated in terms of patient/data samples, as this is a physical medical device (oxygen concentrator) and its performance is evaluated through bench testing and compliance with standards rather than clinical data accuracy. The "test set" here refers to the actual manufactured devices and their components being tested.
• Data Provenance: Not applicable in the context of clinical data. The tests are "internal testing, verification and validation as well as external testing and validation" on the device itself.

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

• Not Applicable. This is a hardware device; its ground truth is based on engineering specifications and direct physical measurements (e.g., oxygen purity, flow rate, power consumption) validated against recognized standards, not subjective expert interpretations of images or data.

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

• Not Applicable. As this isn't a diagnostic accuracy study with human interpretation, there's no need for adjudication of human expert opinions.

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. This is a hardware device, not an AI/ML diagnostic or assistive tool.

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

• Not Applicable. This is a standalone physical device, not an algorithm. Its performance is measured intrinsically.

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

• The "ground truth" for the device's performance is established by engineering specifications, direct physical measurements (e.g., chemical analysis for oxygen purity, flow meters for flow rate), and compliance with recognized industry standards such as ISO 80601-2-69, IEC 60601-1, MIL-STD-810f/g, and USP 93% +/-3% for oxygen purity.

8. The sample size for the training set

• Not Applicable. This is a hardware device; there is no "training set" in the context of machine learning.

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

• Not Applicable. (See #8)

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