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Clamping of a patient's head and neck in a particular position during surgical procedures.

The device uses a four (4) point system to clamp a patient's head during surgical procedures. The material used was selected to reduce or eliminate interference normally associated with all metal skull clamps should x-rays or similar diagnostic activity be utilized during the procedure.

The provided text describes a medical device, the Mizuho Radiolucent Head Frame, and its testing. However, it does not contain the specific information required to complete the detailed acceptance criteria and study description as outlined in your request. The document is a 510(k) summary for a medical device and focuses on establishing substantial equivalence to legally marketed devices.

Here's why the requested information cannot be fully provided from the given text:

• No explicit "Acceptance Criteria" table: The document describes tests performed (Deflection Under Load, Durability of Reusable Skull Pins, Radiolucence) and their results, but it doesn't present them in a structured "acceptance criteria vs. reported performance" table format with numerical targets.
• No detailed information on "Study" for device performance: The tests mentioned are more akin to engineering verification tests rather than a clinical study evaluating diagnostic accuracy or effectiveness in a human-in-the-loop scenario.
  • Sample size and data provenance: Not applicable in the context of the described engineering tests.
  • Experts for ground truth / adjudication: This is irrelevant for the material property and deflection tests conducted.
  • MRMC study: No indication of any multi-reader multi-case study, as the device is a mechanical head frame, not an AI diagnostic tool.
  • Standalone performance: The concept of "standalone performance" as it relates to AI algorithms is not relevant here.
  • Type of ground truth: The "ground truth" for the tests mentioned would be established engineering standards or direct measurements, not clinical outcomes or expert consensus.
  • Training set size/ground truth for training set: These concepts are applicable to machine learning models, not to a physical medical device like a skull clamp.

Based on the provided text, here is what can be inferred and what cannot be provided:

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

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

• Sample Size:
  • Deflection Under Load: Not specified (refers to "The device" and "The metal device," implying one of each type for testing).
  • Durability of Reusable Skull Pins: "sample skull pins" were tested, but the exact number is not specified. They were tested at 5, 10, 20, and 40 cycles.
  • Radiolucence: Not specified (refers to "Representative x-rays").
• Data Provenance: Not specified, but implied to be laboratory/engineering testing performed during the device development process. This is not clinical data.

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

• This information is not applicable to the engineering verification tests described. Ground truth for these tests would be based on physical measurements and material science standards, not expert medical opinion.

4. Adjudication method for the test set:

• This information is not applicable to the engineering verification tests described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No. The document describes engineering tests, not a clinical study involving human readers or AI assistance.

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

• No. This term is specific to AI algorithms and is not applicable to this mechanical device.

7. The type of ground truth used:

• Deflection Under Load: Direct physical measurement against a control (metal device).
• Durability of Reusable Skull Pins: Destructive testing and comparison to unprocessed pins; physical measurement of break load.
• Radiolucence: Visual assessment of x-ray interference.

8. The sample size for the training set:

• This information is not applicable as the device is a physical product, not an AI model requiring a "training set."

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

• This information is not applicable as there is no training set for this device.

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