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VERICIS Physiolog monitors, measures, and/or records physiologic data and patient images from a patient undergoing a cardiac catheterization procedure. VERICIS Physiolog is intended for use in hospital cardiac catheterization laboratories.

VERICIS Physiolog monitors, measures, and/or records physiologic data and patient images from a patient undergoing a cardiac catheterization procedure. VERICIS Physiolog is intended for use in hospital cardiac catheterization laboratories.

Here's an analysis of the acceptance criteria and study information for the VERICIS Physiolog device, based on the provided text:

Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state quantitative acceptance criteria or a direct comparison of its performance against those criteria in a table format. Instead, it describes a functional validation process. The "Predicate device specifications comparison" table, while comparing features, does not represent acceptance criteria met by the device under test.

However, based on the performance data section, we can infer the implicit acceptance criteria were that the device functioned appropriately for the specific tests performed.

Study Details:

1. Sample Size Used for Test Set and Data Provenance:

   • Test Set Sample Size: Not explicitly stated as a number of patients or cases. The testing involved "various types of ECG aberrations," "multiple pressures," "multiple oxygen saturations," "multiple erroneous and/or incongruous entries," and "multiple cases." This suggests a varied test suite but no specific count of simulated data points or cases.
   • Data Provenance: The data was generated using physiologic simulators. This indicates that the data was synthetic/simulated, not derived from human patients. The country of origin is not applicable as it was simulated data. It was retrospective in the sense that the simulated scenarios were pre-defined for testing.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

   • Not applicable. The ground truth for the simulated data points (e.g., correct R-wave detection for a given simulated ECG, correct pressure calculation for a given simulated pressure waveform) would be inherent in the design of the physiologic simulators and the expected output of the device. There's no mention of human experts establishing ground truth for these simulated scenarios.

3. Adjudication Method for the Test Set:

   • Not applicable. Since the ground truth was based on simulator output and expected device behavior, human adjudication of results against a separate expert consensus was not performed or mentioned.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No. This type of study assesses human reader performance, often with and without AI assistance. The testing described for VERICIS Physiolog exclusively focuses on the device's standalone functional performance with simulated data and does not involve human readers or clinical efficacy studies.

5. Standalone (Algorithm Only Without Human-in-the-Loop) Performance:

   • Yes. The described "Performance data" section details testing of the VERICIS Physiolog software and system in a standalone capacity using physiologic simulators. The device's "appropriate response" to various simulated conditions was assessed as purely algorithmic and system function.

6. Type of Ground Truth Used:

   • Simulated data ground truth / Expected algorithmic output: The ground truth for the performance tests was based on the known, expected output of a properly functioning physiologic data recording system given the inputs from the physiologic simulators. This is not expert consensus, pathology, or outcomes data.

7. Sample Size for the Training Set:

   • Not applicable. The document does not describe the use of machine learning or AI models that would require a distinct training set. The device appears to be a rule-based system for monitoring, measuring, and recording physiologic data.

8. How the Ground Truth for the Training Set Was Established:

   • Not applicable, as no training set for a machine learning model is mentioned.

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