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The Effortless Oxygen Conserver Systems are intended to deliver medical-grade oxygen for patients that require supplemental oxygen from oxygen gas sources. Pulsing oxygen allows patients to ambulate longer than they would with a continuous flow regulator on the same cylinder. The Effortless MOBILE may be used in hospital, healthcare facilities, or home care environments. The Effortless PRO is only intended for hospital and healthcare facilities under professional use.

The Effortless Oxygen Conserver Systems (EOCS) is a microprocessor-controlled device, which contains an oxygen pressure regulator and oxygen conserver. Depending upon the model they have been designed for use in ambulatory, home and medical facility settings to supply medical-grade oxygen therapy to patients. The built in oxygen regulator reduces oxygen pressures from an oxygen cylinder or from hospital wall supplies. There are two configurations: The Effortless Pro which connects to hospital wall oxygen and the Effortless Mobile which connects to tanks.

The provided document is a 510(k) Premarket Notification from the FDA for a medical device (Effortless Oxygen Conserver System). This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than proving clinical effectiveness through extensive studies with defined acceptance criteria for individual performance metrics like those typically outlined for AI/ML devices.

Therefore, the document does not contain the detailed information required to answer many of the questions, especially those pertaining to:

• Acceptance criteria table with reported device performance: The document only provides a comparison table of features and specifications between the proposed device and a predicate, not performance metrics against specific acceptance criteria.
• Sample size and data provenance for a test set: No test set (in the context of AI/ML or clinical efficacy) is mentioned. The "bench testing" refers to engineering verification.
• Number of experts and qualifications for ground truth: Not applicable, as there's no mention of expert-established ground truth for a test set.
• Adjudication method: Not applicable.
• Multi-reader multi-case (MRMC) comparative effectiveness study: No such study is mentioned.
• Effect size of human readers improvement with AI assistance: Not applicable, as this is not an AI-assisted device in the diagnostic sense.
• Standalone performance (algorithm only): Not applicable for this type of device.
• Type of ground truth used: Not applicable.
• Sample size for training set and how ground truth was established for training set: Not applicable, as this is not an AI/ML device that requires training.

What the document does describe are several non-clinical tests to demonstrate device performance and safety, primarily for substantial equivalence to a predicate device, not clinical efficacy or AI/ML performance.

Here's an attempt to extract relevant information, acknowledging the limitations based on the document's content:

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

The document does not provide a formal table of "acceptance criteria" and "reported device performance" in the way one might expect for a diagnostic or AI/ML device (e.g., sensitivity, specificity thresholds). Instead, it lists the device's specifications and features, and states that various engineering and safety tests were performed to ensure it met applicable standards and performance requirements.

The "Table of Comparison and Differences" (Page 5-6) serves to show equivalence to the predicate device, not performance against specific criteria. However, for certain parameters, it implicitly suggests an acceptable range by stating what the device accomplishes.

The "acceptance criterion" for this submission is primarily the demonstration of substantial equivalence to the predicate device, not meeting specific quantitative performance thresholds for diagnostic accuracy.

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

Not applicable. The document describes non-clinical bench testing, not a clinical study with patients or a test set of data (e.g., images for an AI/ML device). There is no mention of data provenance as it applies to clinical data.

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

Not applicable. This is not a study requiring expert-established ground truth.

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

Not applicable.

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 study was done, as this is not an AI-assisted diagnostic device.

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

Not applicable. This is not a software-only diagnostic algorithm.

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

For the physical and functional tests, the "ground truth" is measured physical parameters (e.g., pressure, flow rate, trigger sensitivity) compared against engineering specifications and applicable standards (e.g., ISO 80601-2-67).

8. The sample size for the training set

Not applicable. This device does not use machine learning or require a training set.

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

Not applicable.

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