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The VENDYS Model 6000 Model B/BC is a non-invasive digital temperature monitoring device. The device provides continuous monitoring of body surface temperature at multiple sites simultaneously (e.g. fingertips on the study and control hands). It is intended to use for measurement of vascular reactivity by monitoring temperature changes before, during, and after a reactive hyperemia test using a 2-5 minute cuff occlusion procedure.

The VENDYS Model 6000 Model B/BC is a non-invasive digital temperature monitoring device. The device provides continuous monitoring of body surface temperature at multiple sites simultaneously (e.g. fingertips on the study and control hands).

The provided document is a 510(k) clearance letter from the FDA for the VENDYS Model 6000 B B/C device. It does not contain specific information regarding acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert qualifications as requested.

The letter acknowledges the substantial equivalence of the device to a legally marketed predicate device but does not detail the technical performance data or the studies conducted to demonstrate that performance. Therefore, I cannot provide a table of acceptance criteria and reported device performance or other specific study details based on this document.

To answer your request, I would need access to the actual 510(k) submission, which would include the performance data and study reports.

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The VENDYS Model 5000 B/BC is a non-invasive, digital temperature monitoring device. The device provides continuous monitoring of body surface temperature at multiple sites simultaneously (e.g. fingertips on the study and control hands).

It is intended to use for measurement of vascular reactivity by monitoring temperature changes before, during and after a reactive hyperemia test using a 2-5 minute cuff occlusion procedure.

The VENDYS Model 5000 B/BC is a non-invasive, Programmable Diagnostic Computer device capable of providing digital thermal monitoring of multiple digits that can aid in studies of vascular function and evaluation of future cardiovascular risk.

The device provides continuous and simultaneous monitoring of skin surface temperature at multiple digits (e.g. fingertips on the study and control hands). It displays temperature changes before, during and after a reactive hyperemia procedure, and calculates multiple indices based on temperature changes.

The surface temperature signal (1 or 2 channels) is recorded at the digit (e.g. fingertip) and processed in the Vendys Model 5000 B electronics module. The signal processing electronics module (Vendys Model 5000 B) is attached to a PC computer via a standard USB cable through a USB port. The Vendys Data Acquisition and Analysis software is necessary to calculate, display, and archive the test data and is intended to be loaded on an IBM-compatible computer (notebook or desktop). The Vendys software records the signals from the Vendys Model 5000 B electronics module, displays a plot of the temperature response before, during and after a reactive hyperemia procedure, and calculates multiple indices based on temperature changes. These temperature indices and the temperature plot are further explained in Section 12 of the submission. The Vendys software program also allows user input of other pertinent patient information and provides the operator with instructions for use.

The provided text describes the Vendys Model 5000 B/BC, a digital thermal monitor, and its 510(k) summary. However, it does not contain specific acceptance criteria or details of a study proving the device meets acceptance criteria in the format typically requested for medical device performance.

Instead, the document focuses on:

• Device Description and Intended Use: Explaining what the device does and why it's used.
• Predicate Devices: Listing similar devices that were already approved.
• Compliance with Standards: Stating that the device adopted certain FDA Recognized Consensus Standards and voluntary standards (ASTM E112-00, IEC 60601-1, IEC 60601-1-2, ISO10993). This implies that meeting these standards served as a form of "acceptance criteria" for regulatory submission regarding safety and basic performance characteristics like accuracy and EMC.
• Substantial Equivalence Claim: The core of a 510(k) submission, asserting that the new device is as safe and effective as predicate devices.

Given the information, I cannot provide a table with "reported device performance" against explicit "acceptance criteria" where those criteria are detailed numerical targets (e.g., accuracy +/- X degrees, sensitivity Y%, specificity Z%). The document implies that meeting the standards for electronic thermometers (like ASTM E112-00) and substantial equivalence to predicate devices constituted the acceptance criteria for this regulatory submission.

Therefore, the requested information elements related to specific performance studies, sample sizes, ground truth, expert adjudication, or MRMC studies are not present.

Here's a breakdown of what can be extracted or inferred, and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

• Not specified. The document mentions "verification, validations, and safety and performance testing results" but does not detail the size or nature of any specific test sets, nor their provenance (country of origin, retrospective/prospective).
• For a 510(k) of this type (Programmable Diagnostic Computer / Digital Thermal Monitor), the "test set" demonstrating performance would likely involve calibration and accuracy tests against a known standard, as implied by the ASTM standard. However, specifics are missing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable / Not specified. This device is a measurement tool (digital thermometer) rather than an AI diagnostic device where expert ground truth for interpretation is typically required. Its "ground truth" would be highly accurate reference thermometers or devices, and expert interpretation of results is not described as part of its internal performance validation.

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

• Not applicable / Not specified. No adjudication method is described, as the device's function is automated temperature measurement, not interpretive diagnostic output requiring human consensus.

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 not an AI-assisted diagnostic device in the modern sense. It's a digital thermal monitor. It processes signals and calculates indices, but it's not described as having an AI component that would assist human readers in interpretation or diagnosis.

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

• Yes, in essence, the device operates standalone for temperature measurement and index calculation. The device "provides continuous and simultaneous monitoring of skin surface temperature... and calculates multiple indices based on temperature changes." The performance would be assessed purely on its accuracy and precision as a measurement device, without a human-in-the-loop component for its primary function. However, the details of such a standalone study (e.g., accuracy against a gold standard) are not provided.

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

• Implied to be instrument-based reference standards. For a digital thermometer, the ground truth for accuracy and precision would be established using validated reference temperature sources or highly accurate primary standards. It is not expert consensus, pathology, or outcomes data.

8. The sample size for the training set:

• Not applicable / Not specified. The document does not describe a machine learning model that would require a "training set." The device performs calculations based on established physiological principles and algorithms for signal processing, not learned patterns from data.

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

• Not applicable. As no training set for a machine learning model is described, this question is not applicable.

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