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The Needle-Trap™ contains a mechanism that is used to cover the needle point after use of the syringe and needle for their intended functions. In the activated position the Needle-Trap™ guards against accidental needle sticks during normal handling and disposal of the used needle/syringe combination.

The Needle-Trap™ Needle Safety devices are substantially equivalent to the current marketed products

This 510(k) submission for the Needle-Trap™ / Needle Safety Label (K073206) is a pre-market notification for a medical device that claims substantial equivalence to previously marketed devices. It does not contain a study demonstrating acceptance criteria or device performance in the way typically expected for an AI/software as a medical device (SaMD) submission.

Instead, the submission focuses on demonstrating that the device (a syringe anti-needle stick accessory) has similar technological characteristics and performance to existing, legally marketed predicate devices. The "Performance Data" section in the 510(k) summary explicitly states: "Verification and Validation testing confirmed the products meet their specifications." However, it does not provide details on these specifications, acceptance criteria, or the study results themselves.

Therefore, many of the requested items related to a performance study (especially for AI/SaMD) cannot be extracted from this document, as this is a much older and different type of device submission.

Here's an attempt to address your points based on the provided text, while highlighting the limitations:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified in the provided documents. The verification and validation testing mentioned would have used a sample, but the size is not disclosed.
• Data Provenance: Not specified. This type of device (physical accessory) generally undergoes bench testing and possibly human factor studies, rather than relying on patient data in the same way an AI device would.

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

• Number of Experts: Not applicable or not specified. For a physical device like this, ground truth would typically be established through engineering specifications, material testing, and functional performance tests rather than expert interpretation of data.
• Qualifications: Not applicable or not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or not specified. This typically applies to studies where multiple readers assess cases, which is not described here.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done or at least not described in these documents. This type of study is relevant for diagnostic imaging or similar AI applications where human readers' performance is augmented.

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

• Standalone Study: Not applicable. This device is a physical accessory, not an algorithm. Its function is to cover a needle after use, preventing needle sticks. Its performance is inherent to its mechanical design and materials, not a standalone algorithmic output.

7. The Type of Ground Truth Used

• Ground Truth Type: Not explicitly stated as "ground truth" in these documents. For a device like this, the "ground truth" would likely be defined by engineering specifications, functional requirements (e.g., ability to reliably cover the needle, resistance to accidental re-exposure), and safety standards (e.g., ISO standards for sharps injury prevention devices). Performance would be measured against these predefined specifications.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This is not an AI/machine learning device that requires a "training set."

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

• Ground Truth for Training Set: Not applicable. As it's not an AI/ML device, there is no training set or ground truth in that context.

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