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The Mediscope Asymmetrical Compression Staple is intended to be used for fixation such as: mono or bi-cortical osteotomies in the forefoot, first metatarsophalangeal arthrodesis, Akin osteotomy, midfoot and hindfoot arthrodeses or osteotomies, fixation of osteotomies for hallux valgus treatment (Scarf and Chevron), and arthrodesis of the metatarsocuneiform joint to reposition and stabilize metatarsus primus varus.

The Mediscope Compression Staple is a single patient use device made of surgical grade The Medisope Oompression Stapie is cross the fusion site of two bones from the adiacent position. The design features of the Mediscope Compression Staple are summarized below: - Manufactured from nickel-free stainless steel . - 5 sizes . - Barbs to prevent back out .

The provided text describes a medical device, the "Mediscope Asymmetrical Compression Staple and Accessories," and outlines its submission for 510(k) clearance. However, it does not contain information about acceptance criteria or a study proving the device meets specific performance criteria in the way that would typically be described for software or AI-based medical devices (e.g., sensitivity, specificity, accuracy against a ground truth dataset).

Instead, this document focuses on demonstrating substantial equivalence to a predicate device (Wright Medical Compression Staple and Simple Staple, K043059) through engineering testing.

Here's an analysis based on the provided text, addressing your questions where possible:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't define specific "acceptance criteria" for metrics like sensitivity, specificity, or accuracy, because it's not evaluating an AI or diagnostic device in that manner. Instead, the "acceptance criteria" are implied by the performance of the predicate device and the standards followed.

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

• Sample Size: Not specified for the engineering tests. ASTM F564-02 (Standard Specification and Test Methods for Metallic Bone Staples) would outline the required sample sizes for these types of mechanical tests.
• Data Provenance: Not applicable in the context of human data. The "data" here refers to mechanical test results of the device itself.

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

• This question is not applicable. The "ground truth" for a mechanical device is established through physical measurements and adherence to engineering standards (ASTM F564-02) rather than expert human interpretation.

4. Adjudication Method for the Test Set

• This question is not applicable. Adjudication methods (like 2+1, 3+1) are used for resolving disagreements among human readers or experts when establishing ground truth, typically in diagnostic imaging or clinical studies. This document describes objective mechanical testing.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC study was not done. This type of study is relevant for evaluating the impact of AI on human reader performance, which doesn't apply to a physical bone staple.

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

• No, a standalone performance evaluation (in the context of an algorithm) was not done. This document is for a physical medical device, not a software algorithm.

7. The Type of Ground Truth Used

• The "ground truth" for the mechanical performance of the staple is based on:
  • Engineering Standards: Specifically, ASTM F564-02 for mechanical properties (elastic static bending, constant amplitude bending fatigue, pull-out fixation strength).
  • Predicate Device Performance: The Mediscope staple's performance in these tests was compared to that of the already cleared predicate device (Wright Medical Compression Staple). Substantial equivalence implies that its performance met or exceeded the predicate's in a way that doesn't raise new questions of safety or effectiveness.
  • International Standards: ISO 10993-1 (2003)(E) for biocompatibility.

8. The Sample Size for the Training Set

• This question is not applicable. There is no "training set" in the context of this traditional device submission. Training sets are used for machine learning models.

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

• This question is not applicable for the same reason as point 8.

In summary: The provided 510(k) summary focuses on demonstrating substantial equivalence for a physical bone staple by comparing its mechanical performance and biocompatibility to a predicate device and relevant engineering standards, rather than evaluating an AI or diagnostic algorithm against clinical performance metrics.

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Gynecological and (Obstetrical colposcope MEDISCOPE, Diagnostic Device), is intended for magnified viewing of the tissues of cervix.

MEDISCOPE, colposcope ( Obstetrical and Gynecological Diagnostic Device ) is a medical camera unit and accessories to be used to magnify and view tissues of the cervix and diagnose abnormalities and select areas for biopsy. Mediscope consists of a main unit ( power source ) and probe. MEDISCOPE is designed for effective communication and education between doctors and patients. MEDISCOPE can be connected to a TV, CCTV, or Color Video Printer.

This document is a 510(k) premarket notification for a colposcope, which is a medical device. As such, the information provided focuses on demonstrating substantial equivalence to a predicate device and adherence to safety standards, rather than the kind of clinical study data typically associated with novel AI-driven diagnostic devices. Therefore, many of the requested fields regarding acceptance criteria and performance against a gold standard derived from clinical studies will not be found in this type of document.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and the Reported Device Performance

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

This document does not describe a clinical performance study with a "test set" in the context of diagnostic accuracy. The device is a colposcope, a visual magnification tool for examination. The evaluation focuses on safety and technical equivalence to a predicate device, not on diagnostic output.

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

Not applicable. As noted above, this submission is for a medical device (colposcope) that is a tool for visual examination, not a diagnostic AI algorithm. Therefore, no "ground truth" establishment by experts in the context of a diagnostic performance study is described.

4. Adjudication method for the test set

Not applicable. No diagnostic performance test set 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 is not an AI-driven device, and no MRMC study is mentioned.

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

Not applicable. This is not an AI algorithm.

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

Not applicable. The "ground truth" used for this submission is adherence to established medical device standards for design, safety, and equivalence to a legally marketed predicate device.

8. The sample size for the training set

Not applicable. No machine learning model or training set is described.

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

Not applicable. No machine learning model or training set is described.

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