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For the measurement of galvanic skin response.

The development of the Epi-Scan P100 has been based on the combined principles of instrumentation and the electrophysiological effects of innervation of the sweat glands. This provides a noninvasive, painless instrument system fore the quantitative measurement of Selective Tissue Conductance, which has been operationally defined as "…the relative ability of biological tissue to conduct a weak (DC) electrical signal, which is applied for a selected period of time to a selected, limited and restricted surface area of that tissue ... " and which shares those same neuroanatomic reflex pathways as other tests of sympathetic skin activity or regional perspiration levels.

The provided text is a 510(k) premarket notification for the EDX Epi-Scan P100 device, which is a Galvanic Skin Response Measurement Device. This document primarily focuses on establishing substantial equivalence to a predicate device (K874850, Epi-Scan Selective Tissue Conductance Meter) and does not contain detailed information about acceptance criteria or a specific study proving the device meets particular performance metrics.

Therefore, many of the requested details about acceptance criteria, study design, sample sizes, expert qualifications, and ground truth establishment are not present in the provided text. The document is a regulatory submission for market clearance based on equivalence, not a direct report of a performance study with detailed statistical outcomes.

Here's what can be extracted based on the input, with clear indications where information is not available:

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

Explanation: The document does not provide specific quantitative acceptance criteria (e.g., minimum accuracy, specificity, sensitivity values) or numerical performance metrics. Instead, "acceptance" is framed within the context of substantial equivalence to a legally marketed predicate device. The claim is that the technological characteristics of the new device are similar to the predicate, implying that if the predicate performed adequately, the new device will as well.

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 for test set: Not provided. The document does not describe a clinical performance study with a test set of subjects or data.
• Data provenance: Not provided.

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. The document does not describe a study involving expert-established ground truth for a test set. Equivalence is claimed based on engineering and design characteristics compared to a predicate device.

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

• Not applicable. There is no described test set or adjudication process relating to clinical performance.

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 MRMC study was described. The device is a Galvanic Skin Response Measurement Device, not an AI-assisted diagnostic tool for human readers.

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

• The document implies standalone performance comparison of the device's characteristics against the predicate device's characteristics; however, this is not a detailed "standalone performance study" in the typical sense of algorithm-only evaluation for diagnostic accuracy. It's a review of technological features.

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

• Not directly applicable in the conventional sense of a clinical performance study. The "ground truth" for the substantial equivalence claim is the performance and characteristics of the legally marketed predicate device (K874850). The new device's characteristics (indications, skin conductance range, display, components, standards, software) are evaluated against those of the predicate device.

8. The sample size for the training set

• Not provided. The document does not describe a machine learning algorithm that would require a training set.

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

• Not applicable. No machine learning algorithm or training set is described.

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