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The Medicon FIXIT head rest system is designed to provide stabilization and rigid fixation of patient's skull during surgical operations. It is indicated for neurosurgical operative procedures.

The Medicon Fixit Head Rest System is made from one part, not 2 parts as the competitors systems. The Medicon clamp is designed to prevent closing by accident. The pistol handle with safety lock allows the surgeon a one-hand-use of the clamp. Furthermore, the movable parts are reduced to a minimum. The locking mechanism can be loaded with more than 90 kg.

There is a variety of pin holders available. The choice of pin holders very much depends on the preference of the surgeon, the clinical indication and the type of position of the skull.

The pins are made from Titanium and stainless steel and are delivered nonsterile. The pins are re-usable. At the pistol grip side there is a pressure indicator. By turning the screw the surgeon fixes the skull with a fineadjustment.

Hydraulicus allows via a foot pedal to open and close the connector between the head rest clamp and the operating table which enables the surgeon to manipulate the head rest and / or the patient's head manually and at the same time open and close the locking mechanism with the foot peal.

The base unit is connected to the operating table. As the base unit is equipped with a quick coupling, it may be adapted easily and fast to any operating table.

The provided text describes a 510(k) submission for the Medicon Fixit Headrest System, which is a device for neurosurgical head stabilization. This type of submission focuses on demonstrating "substantial equivalence" to existing legally marketed predicate devices, rather than comprehensive de novo clinical studies to prove acceptance criteria based on specific performance metrics.

Therefore, the document does not contain the detailed information requested regarding acceptance criteria, specific device performance, study details (sample size, data provenance, expert qualifications, adjudication methods), MRMC studies, standalone performance data, or training set information.

Instead, the submission relies on the following for demonstrating safety and effectiveness:

1. Table of "Acceptance Criteria" (Implied) and Reported Device Performance

Since this is a 510(k) submission based on substantial equivalence, explicit "acceptance criteria" for novel performance metrics akin to a new drug or efficacy study are not defined. Instead, the "acceptance criteria" are implied by the performance and characteristics of the predicate devices. The "reported device performance" focuses on demonstrating similar functionality and safety characteristics.

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

The document does not describe a test set or any specific study that involved a sample size of patients or data collected from patients. This is a 510(k) summary focused on engineering specifications and comparison to predicate devices, not clinical performance data from a specific test set.

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

Not applicable. No test set involving ground truth established by experts is described.

4. Adjudication method for the test set

Not applicable. No test set involving adjudication is described.

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

Not applicable. This device is a mechanical headrest system, not an AI-assisted diagnostic or therapeutic tool, so MRMC studies involving human readers and AI are irrelevant and not mentioned.

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

Not applicable. This is a mechanical device, not an algorithm, so standalone performance in this context is irrelevant.

7. The type of ground truth used

Not applicable. No ground truth based on pathology, outcomes data, or expert consensus from clinical cases is mentioned or required for this type of submission focused on substantial equivalence of a mechanical device. The "ground truth" for this submission would essentially be the validated performance and safety of the predicate devices.

8. The sample size for the training set

Not applicable. This is a mechanical device, not a machine learning algorithm, so there is no concept of a "training set."

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

Not applicable, as there is no training set for a mechanical device.

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