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The Linde TOSCA System is designed for the simultaneous continuous monitoring of transcutaneous PCO2, functional oxygen saturation SpO2 and pulse rate in adults and pediatrics.

The Linde Tosca System is used for the simultaneous continuous monitoring of transcutaneous PCO2, functional oxygen saturation SpO2 and pulse rate, using a single sensor (Tosca Sensor) applied to the ear lobe.

The system consist of a TOSCA Monitor equipped with an integrated calibration unit which allows a fully automatic calibration and also provides a storage facility for the sensor; a TOSCA Sensor comprising the elements of an electrochemical Stow-Severinghaus-type carbon dioxide sensor and of an optical pulse oximetry sensor; supplies for the sensor preparation; supplies for the sensor attachment at the ear lobe; and a gas mixture for the sensor calibration.

The acceptance criteria and device performance are described in the "Clinical Testing" section ({1}).

1. Table of acceptance criteria and reported device performance:

The document does not explicitly list numerical acceptance criteria or detailed results from the clinical testing. It states a general conclusion that the device meets performance requirements.

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

The provided text does not specify the sample size used for the clinical test set or the data provenance (country of origin, retrospective/prospective nature of the 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):

This information is not provided in the document.

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

This information is not provided in the document.

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 document describes a medical device for monitoring physiological parameters (PCO2, SpO2, Pulse Rate), not an AI-assisted diagnostic or interpretive tool that would typically involve human readers. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and not mentioned.

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

The document describes the performance of the "Tosca PCO2, SpO2 and Pulse Rate Monitoring System" as a whole, which includes the sensor and monitor. This would be considered the standalone performance of the device itself for its intended function (continuous monitoring of PCO2, SpO2, and pulse rate). It's not an "algorithm only" study as it involves hardware and sensor technology.

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

The document does not explicitly state the type of ground truth used for the clinical testing. For physiological monitoring devices like this, ground truth would typically come from:

• Reference standard devices: Comparing the Tosca system's readings for PCO2, SpO2, and pulse rate against established, highly accurate reference measurement methods (e.g., arterial blood gas analysis for PCO2, CO-oximetry for SpO2, or ECG for pulse rate).
• Clinical observation: Assessing the device's ability to accurately reflect a patient's physiological state as determined by other clinical assessments.

Given the nature of the device, it's highly probable that a comparison against established reference methods was used, however, this is an inference, not explicitly stated.

8. The sample size for the training set:

This information is not provided in the document. The document describes clinical testing, implying validation or verification, rather than the development and training of a learning algorithm.

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

This information is not provided, as the document does not mention a "training set" in the context of machine learning or AI development.

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