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The indications for use for the Gas Module 3 include monitoring of airway gases during anesthesia and/or assisted respiration. The intended environment of use is the anesthesia department, including the Operating Room (OR) and post anesthesia care units (PACU), etc.

The Gas Module 3 is an independently powered unit capable of interfacing with Mindray DS USA, Inc. (hereafter MRDS) Patient Monitors using a proprietary communications protocol. The system connects to the patient monitor via a RS232 connector. All Gas Module 3 data display on the monitor screen. And all user commands are entered on the monitor and then electronically transmitted to the Gas Module 3.

The Gas Module 3 measures in real-time, breath-by-breath CO2, O2, N2O gases. Additionally, the Gas Module 3 monitors the anesthetic agents Halothane (HAL), Isoflurane (ISO), Sevoflurane (SEV), Desflurane (DES) and Enflurane (ENF).

The Gas Module 3 consists of AION Multigas Analyzer, Servomex Paramagnetic Oxygen sensor, Power supply Board and Communication Board.

The subject device is a modified version of a previously cleared model, the Gas Module SE. The Gas Module SE was cleared under K062754, on September 13, 2006.

The provided text is a 510(k) summary for the "Gas Module 3" device (K180788). This document primarily focuses on establishing substantial equivalence to a predicate device (Gas Module SE, K062754) based on non-clinical testing. It does not describe an acceptance criteria study in the conventional sense of establishing performance against a predefined clinical or statistical threshold.

Instead, the study outlined is a series of non-clinical bench tests and EMC/Electrical Safety tests designed to demonstrate that modifications to the predicate device still meet existing specifications and standards, and that its performance is equivalent to the predicate. There is no mention of a human-in-the-loop study, expert ground truth establishment, or specific sample sizes for clinical data because this is a non-clinical performance evaluation for a monitoring spirometer, not an AI/software-as-a-medical-device (SaMD) study involving ground truth or human reader performance.

Therefore, many of the requested fields cannot be directly populated from the provided text as they are not applicable to the type of device and study described.

Here's the information that can be extracted or deduced from the provided document:

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

The document details performance changes and states they meet relevant standards. It frames the "acceptance criteria" not as novel thresholds, but as continued adherence to existing specifications and consensus standards.

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

• Sample Size: Not explicitly stated. The testing appears to be bench testing of the device itself, likely involving controlled gas mixtures and simulated breathing patterns rather than human or animal subjects.
• Data Provenance: Not specified, but given the manufacturer (Shenzhen Mindray Bio-medical Electronics Co., Ltd.) it's likely conducted in China. The testing is non-clinical bench testing.

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. This is not a study requiring expert-established ground truth for performance evaluation of classification or diagnosis. It's a non-clinical engineering and performance validation.

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

• Not applicable. This is not a study requiring adjudication of expert interpretations.

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. This is a non-clinical device performance study, not an AI/SaMD study involving human readers.

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

• This device is a hardware gas module that measures gases. While it has software, the performance evaluation described pertains to the measurement accuracy and response times of the physical sensing system, not an "algorithm only" performance in the context of AI/SaMD. The performance metrics are direct measurements, not algorithmic outputs from complex data.

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

• The "ground truth" for this device's performance would be established by reference laboratory standards and calibrated gas mixtures for accuracy and precision measurements, as per relevant ISO standards (e.g., ISO 80601-2-55). It is not based on expert consensus, pathology, or outcomes data.

8. The sample size for the training set

• Not applicable. This is a monitoring device, not a machine learning/AI algorithm that requires a "training set" in the conventional sense. Software verification and validation were performed, but this is distinct from training an AI model.

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

• Not applicable, as there is no "training set."

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