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The BEAR® 1000 Ventilator is fully capable of ventilating the entire spectrum of patients requiring such support-from the Emergency Room to the ICU, from pediatric patients to adults.

The BEAR® 1000 Ventilator employs both microprocessor and microcontroller technology to vield a simple, compact design. This full-featured package has powerful performance capabilities-in terms of output capability, Sensitivity to patient demand and response time. They render the BEAR® 1000 Ventilator fully capable of ventilating the entire spectrum of patients requiring such support-from the Emergency Room to the ICU, from pediatric patients to adults.

The modifications to the Bear 1000 % Ventilator are minor, and primarily affect only the monitoring capabilities of the system, with the exception of the addition of one alarm. There is no impact to the control functions of the ventilator. These modifications are:

The addition of esophageal and tracheal pressure monitoring. All monitoring is the result of pressure data that is obtained through the esophageal and tracheal catheters and communicated to the Graphic Display via the RS-232 communication port.

The addition of one alarm -- Prolonged Esophageal Pressure.

The addition of one membrane key switch for filling the esophageal catheter balloon with air.

The addition of a bi-directional flow sensor at the patient wve using a presently approved and marketed technology to enhance the flow and volume monitoring capabilities. The bidirectional flow sensor measures inspiratory and expiratory flow that is used in calculating and displaying digital data and various waveforms/loops on the Graphic Display. The user has the option of using either the bi-directional flow sensor or the exhaled flow sensor.

The digital data calculated by and displayed on the Graphic Display has been enhanced to include 17 additional respiratory parameters which are categorically displayed on four user selected pages.

The provided text is a 510(k) summary for the BEAR 1000es Ventilator, which describes modifications to an existing device (BEAR 1000 Ventilator) by adding BICORE monitoring technology. This document focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and validation study results in the manner typically seen for novel medical device clearances today.

Therefore, the requested information elements related to formal acceptance criteria, device performance tables, expert adjudication, MRMC studies, standalone performance, and ground truth establishment are not explicitly stated or detailed in this 510(k) summary.

Specifically, the document primarily discusses modifications to the device (new monitoring capabilities, an additional alarm, a key switch, and a bi-directional flow sensor), the electronic and pneumatic overview, and a comparison to predicate devices. It states that the modifications are "minor" and "primarily affect only the monitoring capabilities of the system, with the exception of the addition of one alarm."

However, it does describe the "Summary of Performance Testing" as follows:

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

• Acceptance Criteria (Implicit): The software was qualified by performing formal qualification procedures to verify the ventilator's performance "in accordance with the approved Design and Performance Specification." The Graphic Display qualification also involved a "complete verification of all features as specified in the Design and Performance Specification."
• Reported Device Performance: The document states that the software qualification resulted in "software Release 1.03." It also notes that "a FMEA was done for the Flow PCB and concluded that there are no risk associated with any potential failures of components on the PCB." No specific quantitative performance metrics (e.g., accuracy, precision) against defined acceptance ranges are provided in this summary.

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

• Not specified. The document mentions "a test ventilator" for software qualification but does not give a sample size, data provenance, or whether the testing 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/Not specified. This type of expert assessment for ground truth is typically relevant for diagnostic imaging or AI algorithms for patient diagnosis, which is not the primary focus of this ventilator's approval. The "ground truth" for a ventilator's performance would likely be its adherence to engineering specifications and physiological parameters.

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

• Not applicable/Not specified.

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 medical device (ventilator) with added monitoring capabilities, not a diagnostic imaging AI tool that would typically involve human "readers" and MRMC studies.

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

• The "software for the ventilator was qualified by performing formal qualification procedures on a test ventilator" and a "complete formal qualification was performed on the Graphic Display." This constitutes standalone testing of the device's software and hardware functions. However, no specific details on "algorithm only" performance are provided beyond general qualification.

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

• Ground Truth (Implicit): The "approved Design and Performance Specification" serves as the implicit ground truth against which the device's functional performance and new features were verified. For the FMEA, the "absence of risk associated with any potential failures" was the conclusion.

8. The sample size for the training set

• Not applicable. This document describes the qualification and testing of a hardware and software system for a ventilator, not a machine learning model that requires explicit training data. The "training" here would refer to the development and iterative testing of the software during the engineering process.

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

• Not applicable. (See point 8).

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