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Catheter for drainage of blood and fluid from mediastinum after chest surgery and/or trauma; for use with suction apparatus

Silicone perforated tubing, flattened profile, blue radiopaque stripe; surface treated to reduce friction and Antimicrobial; perforations ("eyes") on both flattened surfaces of tubing; external end has cutaway adapter; imprinted with drain size

This document describes a 510(k) submission for an "Antimicrobial Mediastinal Silicone Drain." However, it is not a study involving AI or a medical imaging device.

Therefore, I cannot provide the information requested in your prompt as it pertains to AI/ML device studies. The prompt asks for details like:

• Acceptance criteria and reported device performance in the context of AI/ML metrics.
• Sample size and provenance for test sets for AI/ML models.
• Number of experts and their qualifications for establishing AI/ML ground truth.
• Adjudication methods for AI/ML ground truth.
• Multi-reader multi-case comparative effectiveness studies for AI/ML assistance.
• Standalone performance of an algorithm.
• Type of ground truth used for AI/ML training/testing.
• Sample size and ground truth establishment for AI/ML training sets.

The provided text describes a catheter with an antimicrobial coating. The "study" mentioned is a microbiological challenge test to prove the effectiveness of the antimicrobial surface against specific organisms.

Here's what I can extract from the provided text, re-framed to directly answer the specific questions where applicable, and noting where the information is not present or irrelevant to AI/ML:

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

Acceptance Criteria & Reported Performance (for Antimicrobial Efficacy):

(Note: The document doesn't explicitly state quantitative acceptance criteria like "X% reduction in bacterial count." It only states the device "proved effective" against the listed organisms through a challenge.)

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

• Sample Size (for Antimicrobial Challenge Test): Not specified in the document. This would typically refer to the number of device samples tested in the lab.
• Data Provenance: Not specified, but generally, these are laboratory-based studies, not patient data.

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)

• Number of Experts & Qualifications: Not applicable. Ground truth for an antimicrobial challenge test is established in a microbiology laboratory by testing the device against bacterial cultures, not by human experts interpreting medical images.

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

• Adjudication Method: Not applicable. This concept pertains to resolving discrepancies in expert interpretations of medical images, which is not relevant here.

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

• MRMC Study: No. This is not an AI-assisted device.

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

• Standalone Performance: No. This is not an AI algorithm.

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

• Type of Ground Truth: The "ground truth" (or outcome measure) for this device is the inhibition or killing of specific microorganisms when they are exposed to the device's surface in a controlled laboratory setting (microbiological culture results).

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. There is no AI model or training set involved.

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

• Ground Truth for Training Set Establishment: Not applicable. There is no AI model or training set involved.

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