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The CNMC Model 206 is a medical dosimeter, or medical electrometer that is capable of performing measurements of diagnostic and therapeutic amounts of ionizing radiation when an appropriate ionization chamber or diode dosimeter is connected. These instruments are used exclusively by qualified personnel, typically medical physicists, for the calibration and quality control of medical equipment that produces ionizing radiation.

The CNMC Model 206 is a dosimetry electrometer intended for measuring the output charge of an Ionization chamber or dosimetry diode that is used in a radiotherapy beam.

The CNMC Model 206 is a medical dosimeter, or medical electrometer that is capable of performing measurements of diagnostic and therapeutic amounts of ionizing radiation when an appropriate ionization chamber or diode dosimeter is connected. These instruments are used exclusively by qualified personnel, typically medical physicists, for the calibration and quality control of medical equipment that produces ionizing radiation.

Here's an analysis of the provided text regarding the acceptance criteria and study for the CNMC Model 206 Electrometer/Dosimeter:

General Note: The provided document is a 510(k) Premarket Notification Summary from the FDA. This document primarily focuses on establishing "substantial equivalence" to a predicate device, rather than providing a detailed report of a new clinical study with specific acceptance criteria and performance data for a novel device. Therefore, many of the requested items (e.g., sample sizes, ground truth establishment, expert qualifications, MRMC studies) are not typically found in this type of regulatory submission because the device is not a complex AI/ML system requiring extensive clinical validation. Instead, the focus is on demonstrating that the new device performs comparably to an already approved device.

Description of Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

Summary: The CNMC Model 206 Electrometer/Dosimeter is primarily evaluated for "substantial equivalence" to a predicate device (Sun Nuclear Model 1010/206 – K002444). The acceptance criteria are implicitly defined by the specifications of the predicate device and the new device's ability to meet those specifications. The "study" demonstrating this involves comparing the technical specifications and intended use of the CNMC Model 206 to the predicate device.

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

Since this is a substantial equivalence submission, the "acceptance criteria" are the performance specifications of the predicate device, and the "reported device performance" is how the CNMC Model 206 matches or is equivalent to those specifications.

Conclusion from document: The Model 206 is substantially equivalent to the predicate device because:

1. The indication for use of the Model 206 is exactly the same as the predicate device.
2. The units are of identical design.
3. The Model 206 and the predicate device are tested to the same specifications.
4. The Model 206 and the predicate device use the same vendors for purchasing of material for manufacturing.
5. CNMC considers the Model 206 equivalent in all areas to the predicate device.

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

This information is not provided in the document. For a substantial equivalence claim for a comparatively simple electrometer, detailed "test set" data in the context of clinical populations or image datasets is typically not required. The "test" here refers to whether the device's technical specifications match those of the predicate. Data provenance, if any, would be from internal engineering testing.

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 applicable/provided in the document as there isn't a "ground truth" established by experts in the context of diagnostic interpretation (like in AI/ML products). The "ground truth" for an electrometer is its physical measurement accuracy, which is verified against calibrated standards, not human expert consensus.

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

This information is not applicable/provided. Adjudication methods are relevant for subjective assessments, particularly in medical imaging or clinical diagnosis. This device measures physical quantities and its performance is assessed against known standards, not through human adjudication of results.

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 information is not applicable/provided. The device is an electrometer/dosimeter, not an AI-assisted diagnostic tool. Therefore, MRMC studies and "human readers improving with AI" are entirely outside the scope of this device and its 510(k) submission.

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

This information is not applicable/provided. This device is a measurement instrument, not an algorithm. Its performance is always "standalone" in that it produces a measurement. The "human-in-the-loop" is the qualified personnel (medical physicists) who interpret and utilize the measurements for calibration and quality control.

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

The "ground truth" for an electrometer's performance is typically established by traceable physical standards and calibrated reference instruments. The document doesn't explicitly state the methodology for this, but it implies that the device is "tested to the same specifications" as the predicate, which would involve comparison to these physical standards.

8. The sample size for the training set

This information is not applicable/provided. This device is a hardware measurement instrument and does not use a "training set" in the context of machine learning or AI.

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

This information is not applicable/provided for the same reasons as item 8.

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