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SurgiClear™ is intended to cover and protect a wound caused by percutaneous medical devices such as drains, chest tubes, orthopedic pins, fixtures, and wires.

SurgiClear™ may also be used to cover and secure primary dressing.

SurgiClear™ inhibits microbial growth within the dressing and prevents external contamination.

SurgiClear™ is composed of a clear polyurethane film coated with a silicone adhesive containing chlorhexidine and silver salts.

This document (K121819) is a 510(k) Premarket Notification for a medical device called SurgiClear™ Antimicrobial Clear Silicone Adhesive Dressing with Chlorhexidine and Silver, submitted by Covalon Technologies Inc. in 2012.

The 510(k) summary focuses on demonstrating that the new device is "substantially equivalent" to already legally marketed predicate devices, rather than providing detailed acceptance criteria and study results in the format requested, which is more typical for AI/ML device submissions.

Here's an attempt to extract and interpret the information provided in the context of your request, with significant caveats that the level of detail is much lower than what would be expected for AI/ML performance studies:

Acceptance Criteria and Reported Device Performance

The provided document does not specify quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, accuracy) because it's a submission for a physical medical product (a wound dressing), not an AI/ML diagnostic or predictive algorithm.

Instead, the "acceptance criteria" are implied by the battery of tests performed to demonstrate safety and effectiveness for a physical device, and the "reported device performance" is that the device met these criteria, leading to a determination of substantial equivalence.

Implied "Acceptance Criteria" and "Reported Device Performance" (based on tests conducted):

Study Details (Based on available information)

Given this is a physical medical device and not an AI/ML algorithm, many of the requested points are not applicable or the information is not provided in the document.

2. Sample size used for the test set and the data provenance:
* Test Set (In vitro log reduction): Not specified. This typically involves standardized bacterial cultures.
* Test Set (Biocompatibility): Not specified. These studies typically use cell cultures (cytotoxicity), animal models (systemic toxicity, sub-chronic toxicity), and guinea pigs/rabbits (sensitization, irritation).
* Test Set (Porcine wound healing study): Not specified, but involved porcine models. Data provenance is implied as in-vivo animal study.
* Test Set (Human repeat insult patch test): Not specified, but involved human subjects. Data provenance is implied as prospective clinical study (human subjects).
* Country of Origin: Studies likely conducted in Canada or via contract research organizations for a Canadian company.

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:
* Not applicable in the AI/ML sense. Ground truth for these studies would be established by laboratory technicians, pathologists, and clinicians/toxicologists, following established scientific and regulatory protocols rather than "expert consensus" on imaging or data interpretation.

4. Adjudication method for the test set:
* Not applicable. Adjudication methods like 2+1 or 3+1 are used for human interpretation discrepancies in AI/ML studies. For these types of device tests, results are typically objective measurements or observations interpreted by trained personnel according to pre-defined criteria.

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 physical wound dressing and does not involve human readers or AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
* Not applicable. This is not an algorithm.

7. The type of ground truth used:
* In vitro log reduction: Measured bacterial count reductions.
* Biocompatibility: Histopathological findings, cytotoxicity assays, irritation scores, systemic toxicity measurements, sensitization reactions.
* Porcine wound healing study: Clinical observations of wound healing, possibly histopathology.
* Human repeat insult patch test: Clinical assessment of skin reaction (e.g., erythema, edema) by dermatologists or trained clinicians.

8. The sample size for the training set:
* Not applicable. This is a physical device, not an AI/ML algorithm requiring a training set.

9. How the ground truth for the training set was established:
* Not applicable.

Conclusion:

The provided document is a 510(k) summary for a physical medical device (wound dressing) and therefore does not contain the type of detailed information about acceptance criteria, sample sizes, expert ground truth, or study designs that would be relevant for an AI/ML medical device submission. The performance testing section mentions several standard biological and clinical studies (in vitro log reduction, biocompatibility, porcine wound healing, human repeat insult patch test) which confirmed the device's safety and effectiveness, leading to a determination of substantial equivalence to predicate devices.

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