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The 3M™ Tegaderm™ CHG Chlorhexidine Gluconate Gel Pad can be used to cover and protect catheter sites. Common applications include covering intravascular catheters and percutaneous devices.

The 3M™ Tegaderm™ CHG Chlorhexidine Gluconate (CHG Gel Pad) is used to cover and protect vascular and non-vascular percutaneous medical devices. The CHG Gel Pad is breathable and transparent, allowing continuous site observation.

The CHG Gel Pad contains 2% w/w Chlorhexidine Gluconate (CHG), an antiseptic agent with broad spectrum antimicrobial and antifungal activity. The CHG Gel Pad absorbs fluid. In vitro testing (log reduction) demonstrates that the CHG Gel Pad has an antimicrobial effect against a variety of gram-positive and gram-negative bacteria, mold, and yeast.

This document is a 510(k) Pre-market Notification for a medical device called the "3M™ Tegaderm™ CHG Chlorhexidine Gluconate Gel Pad". It is a submission to the FDA seeking clearance to market the device, claiming substantial equivalence to a previously cleared predicate device.

Here's an analysis of the provided text in relation to your request:

Based on the provided text, the device is not an AI/ML device, therefore, much of the requested information (sample sizes for training/test sets, ground truth establishment for AI, MRMC studies, details on expert readers/adjudication) is not applicable. The device is a medical dressing with an antiseptic agent.

My analysis will focus on the information relevant to a non-AI medical device submission.

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly list "acceptance criteria" in a quantitative manner as one might find for an AI algorithm's performance metrics (e.g., sensitivity, specificity thresholds). Instead, for this type of device, "acceptance criteria" are implied by successful completion of standard performance and biocompatibility tests, demonstrating that the device functions as intended and is safe. The "reported device performance" refers to the outcomes of these tests.

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

• Test Set Sample Size: The document does not specify discrete "sample sizes" for the in vitro tests beyond stating that testing was performed. For in vitro laboratory tests, samples usually refer to a certain number of product units tested, which is not detailed here. Clinical data from human subjects is absent because this is a 510(k) submission based on substantial equivalence and non-clinical testing.
• Data Provenance: The in vitro tests were conducted internally by 3M Company, presumably in a laboratory setting. There is no mention of country of origin of the data in the context of clinical trials (as there are none) or whether the data is retrospective or prospective.

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

This question is not applicable as the device is not an AI/ML device relying on expert-labeled data for its performance evaluation. The "ground truth" for this device's performance is established by the direct measurement of physical and chemical properties in laboratory settings (e.g., direct measurement of CHG concentration, fluid absorption, bacterial kill rates).

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

This question is not applicable for the same reasons as #3. Adjudication methods are typically used in clinical trials or for ground truth establishment in AI/ML performance evaluation involving human interpretation, which is not the case 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

This question is not applicable as the device is not an AI/ML device. MRMC studies are used to evaluate diagnostic accuracy and the impact of AI assistance on human readers, which is entirely outside the scope of this medical dressing.

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

This question is not applicable as the device is not an AI/ML algorithm.

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

For this device, the "ground truth" is derived from:

• Direct physical and chemical measurements: Examples include the measured concentration of Chlorhexidine Gluconate, measured fluid absorption capacity, and measured moisture vapor transmission rate.
• Microbiological assays: For the in vitro direct time kill studies, the "ground truth" refers to the confirmed reduction in microbial counts based on standardized microbiological testing protocols.
• Biocompatibility standards: Compliance with ISO 10993-1 and FDA Guidance based on established toxicological principles and testing.

8. The sample size for the training set

This question is not applicable as the device is not an AI/ML device and therefore does not have a "training set" in that context. The device's characteristics are inherent to its manufacturing and materials.

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

This question is not applicable for the same reasons as #8.

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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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