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The UP-GRADE FOREHEAD THERMOMETER is a non-sterile, reusable clinical thermometer intended for the determination of human's body temperature using the forehead as measurement site.

The over-the-counter Up-Grade Forehead Thermometer is a compact predictive clinical thermometer designed to measure human body temperature by detecting heat flow from the temporal artery, by using the heat conduction principle and prediction. The over-the-counter Up-Grade Forehead Thermometer is designed to calculate the maximum temperature of a probe in contact with the body site. without waiting for thermal equilibrium to occur, by heat transfer data and mathematical algorithm. The temperature reading range is from 35.0ºC to 42.0°C (95.5°F to 107.6°F) and the time of measurement is 6-10 seconds.

Here's a breakdown of the acceptance criteria and study information for the UP-GRADE FOREHEAD THERMOMETER based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document refers to compliance with voluntary standards, but does not explicitly state the specific acceptance criteria or provide a table of performance metrics. However, it does state that the device's acceptable performance was established through comparative testing with market-cleared devices and compliance with the following standards:

Note: The specific performance metrics (e.g., accuracy, repeatability) that would typically be detailed within compliance to ASTM E1112 are not explicitly listed in the summary. The summary only states that performance was "established" through testing and standard compliance.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the sample size used for the clinical or non-clinical test set. It mentions "comparative testing with market-cleared devices," implying a clinical validation, but provides no details on the number of subjects or the nature of the data (prospective/retrospective or country of origin).

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

This information is not provided in the 510(k) summary.

4. Adjudication Method for the Test Set

This information is not provided in the 510(k) summary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No MRMC study is mentioned in the 510(k) summary. The device is a standalone thermometer, 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

Yes, a standalone performance assessment was effectively done. The device itself is an "algorithm only" in the sense that it uses a "mathematical algorithm" to calculate maximum temperature. The non-clinical and clinical tests performed were to establish the safe and effective performance of this device in a standalone manner, without human interpretation of its output beyond reading the displayed temperature.

7. The Type of Ground Truth Used

The document doesn't explicitly state the "ground truth" method. However, for clinical thermometers, the standard practice for establishing accuracy typically involves comparison against a reference thermometer (e.g., a rectal thermometer) considered the gold standard for core body temperature measurement in a clinical setting. Given the mention of "comparative testing with market-cleared devices" and compliance with ASTM E1112 (which defines accuracy requirements for electronic thermometers), it's highly probable that the ground truth was established by comparing the device's readings to those of a validated reference thermometer.

8. The Sample Size for the Training Set

The document does not specify any training set size. As a non-AI or machine learning-driven device in the modern sense, it likely does not have a "training set" in the way an image recognition algorithm would. Its "mathematical algorithm" for prediction would have been developed and validated, but not "trained" on a specific dataset in the typical AI/ML context.

9. How the Ground Truth for the Training Set Was Established

As no "training set" in the AI/ML context is implied or mentioned, the method for establishing its ground truth is not applicable or provided. The "mathematical algorithm" would have been designed based on heat transfer principles and validated against actual temperature measurements.

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