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The bubble humidifier is a device that is intended to add moisture to breathing gases for administration to a patient. The bubble humidifier is indicated for use with oxygen concentrators or gas sources in homecare, hospital, extended care facilities and hospice environments. The bubble humidifier is a non-sterile device indicated for single-patient usage. The device is indicated for patients who require humidification of high flow supplemental breathing gases.

The Salter Labs Bubble Humidifier (High Flow) is an empty, disposable, non-sterile device that is intended to humidify breathing gas prior to delivery to a patient. The Salter Labs Bubble Humidifier (High Flow) is provided with a 6 PSI safety valve and can operate within flow rates of 6 to 15 LPM. The device is used with various breathing gas sources (i.e., oxygen concentrators, gas cylinders and wall outlets) and provides connection for delivery of humidified breathing gas via face masks and cannulas, and use of optional oxygen tubing and water traps. The device is made of a humidifier bottle which is used to hold water during use, a lid which seals the humidifier bottle and houses the different interface connectors, an audible pressure relief mechanism to notify the user of a downstream occlusion and a diffuser located inside the humidifier bottle that is designed to uniformly disperse the gas throughout the water. The 360° diffuser ports also provide quiet operation and minimize system backpressure. Both the bottle and lid are constructed to be easy to grip and reduce the chance of cross threading. The jar is permanently marked with minimum/maximum water levels.

Here's a breakdown of the acceptance criteria and study information for the Salter Lab Bubble Humidifier (High Flow), based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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 does not explicitly state a specific "sample size for the test set" or the "country of origin of the data" or whether the testing was "retrospective or prospective." The non-clinical testing appears to be a series of laboratory tests performed on the device itself against established standards.

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 applicable to a physical medical device like a humidifier, where performance is measured against technical specifications and industry standards rather than being subject to expert interpretation for ground truth.

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

This information is not applicable to the non-clinical testing of a physical medical device against performance standards.

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

This information is not applicable. The device is a bubble humidifier, not an AI-powered diagnostic tool requiring human reader studies.

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

This information is not applicable. The device is a physical medical device, not an algorithm.

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

The "ground truth" for this device's performance is based on established industry standards and regulatory requirements, specifically:

• ASTM F1690-96 (Reapproved 2004), "Standard Specification for Humidifiers for Medical Use-Part 1: General Requirements for Active Humidification Systems" for safety and performance and humidifier output.
• ISO 10993, "Biological Evaluation of Medical Devices" for biocompatibility.
• The 6 PSI pressure relief valve specification.
• The presence of an audible occlusion notification.

8. The sample size for the training set

This information is not applicable. The device is a physical medical device, not a machine learning model that requires a training set.

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

This information is not applicable, as there is no training set for a physical medical device.

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