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The Perioperative Data Management System (PDMS) may be used as a data management system for electronically collecting, recording, and displaying anesthesia information.

The Perioperative Data Management System is indicated for electronically collecting, displaying, and recording perioperative information. Federal law restricts this device to sale by or on the order of a physician.

The PDMS may be used for receiving, recording and displaying monitoring information from anesthesia systems, other medical devices in the operating room (OR), and through the hospital network and manual entry.

The provided text describes a 510(k) submission for a Perioperative Data Management System (PDMS) and its determination of substantial equivalence to a predicate device, the O.R. Data Manager (ORDM). It does not contain specific acceptance criteria, performance data, or details of a study that directly proves the device meets such criteria in terms of quantitative performance metrics (e.g., sensitivity, specificity, accuracy).

The document focuses on the regulatory process of establishing substantial equivalence for medical devices, which is a different type of evaluation than a clinical or performance study establishing specific quantitative acceptance criteria.

Therefore, I cannot populate the table or answer most of your questions based only on the provided text.

Here's a breakdown of what can and cannot be answered:

1. A table of acceptance criteria and the 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)

• Cannot be determined from the text. The document describes a regulatory submission for substantial equivalence, not a performance study with a test set of data.

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)

• Cannot be determined from the text. No ground truth establishment is described for a performance test set.

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

• Cannot be determined from the text. No adjudication method is described.

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 evidence of an MRMC study. This device is a data management system, not an AI-assisted diagnostic tool that would typically undergo an MRMC study with human readers.

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

• Cannot be determined from the text. While the device "receives, records, and displays" data, its core function is data management, not an algorithm performing a standalone diagnostic or predictive task in the way your question implies. The document describes "system level qualification testing," but not in the context of a standalone algorithm performance study.

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

• Cannot be determined from the text. No ground truth is mentioned. The evaluation for substantial equivalence relied on comparing the PDMS's intended use and principle of operation to a legally marketed predicate device (ORDM).

8. The sample size for the training set

• Not applicable / Cannot be determined from the text. The device is a data management system, not described as using machine learning or needing a "training set" in the context of AI.

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

• Not applicable / Cannot be determined from the text. (See point 8).

Summary of what the document does indicate regarding device evaluation:
The evaluation of the PDMS for 510(k) clearance primarily focused on demonstrating its substantial equivalence to a pre-existing device (O.R. Data Manager K900937). This was supported by:

• Sharing the same intended use.
• Sharing the same principle of operation.
• Undergoing a hazard analysis.
• Completing system level qualification testing.
• Performing environmental testing.
• Completing electromagnetic compatibility testing.

These are standard regulatory steps for a Class II device and do not typically involve the type of quantitative performance studies with specific accuracy/sensitivity/specificity metrics, ground truth, and expert evaluation that your questions imply for AI/diagnostic devices.

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