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ColActive Ag™ Collagen with Silver, Antimicrobial Dressing is indicated for the management of full and partial thickness wounds including: Pressure ulcers, Diabetic ulcers, Ulcers caused by mixed vascular etiologies, Venous ulcers, First and Second degree burns, Donor and graft sites, Abrasions, Dehisced surgical wounds, Traumatic wounds healing by secondary intention.

ColActive Ag™ Collagen with Silver, Antimicrobial Dressing is an advanced wound care dressing composed of collagen, sodium alginate and silver chloride provided in a sterile sheet or rope form. ColActive Ag™ Collagen with Silver, Antimicrobial Dressings are pliable, absorbent dressings that absorb moisture such as wound fluid forming a gel, thus maintaining a moist environment at the wound surface that aids in the formation of granulation tissue and epithelialization. The dressings act as an effective barrier to bacterial and fungal penetration. The silver content is intended to prevent colonization of the dressing. The dressings can be cut to fit specific wounds and are able to be layered for the management of deep wounds.

The provided document is a 510(k) summary for the ColActive Ag™ Collagen with Silver, Antimicrobial Dressing, which focuses on demonstrating substantial equivalence to previously marketed wound dressings. This type of regulatory submission does not typically include detailed clinical studies or performance data with acceptance criteria in the way envisioned by the prompt for an AI-based device. Instead, it relies on demonstrating similar characteristics and safety to existing devices.

Therefore, many of the requested data points (like sample size for test sets, number of experts for ground truth, MRMC studies, standalone performance of an algorithm, training set size, etc.) are not applicable to this 510(k) submission for a wound dressing.

However, I can extract the information that is relevant to the document provided, focusing on what was included to demonstrate safety and equivalence.

Here's a breakdown based on the provided text, acknowledging the limitations for an AI-centric evaluation:

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

The document does not specify quantitative "acceptance criteria" for device performance in the same way an AI device might have metrics like sensitivity or specificity thresholds. Instead, it focuses on demonstrating biocompatibility and substantial equivalence to predicate devices. The "performance" is primarily shown through the successful completion of standard biocompatibility tests.

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 Test Set: Not specified in the 510(k) summary. Biocompatibility tests (e.g., cytotoxicity, sensitization, irritation) typically involve in vitro and in vivo animal studies (using specific numbers of cells/animals per test), but these details are not provided here.
• Data Provenance: Not specified, but generally, ISO 10993 tests are conducted in certified laboratories. The country of origin of the data is not mentioned.
• Retrospective or Prospective: Not applicable in the context of biocompatibility testing for this wound dressing.

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

• Number of Experts: Not applicable. Biocompatibility testing relies on standardized protocols and laboratory analysis, not expert consensus on medical imaging or diagnoses.
• Qualifications of Experts: Not applicable in this context.

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

• Adjudication Method: Not applicable. Biocompatibility testing has objective endpoints measured in a lab, not subjective interpretations requiring adjudication.

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 type of study is not relevant for a wound dressing. This is a medical device, not an AI diagnostic or assistive tool for human readers.

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

• Standalone Performance: Not applicable. This is a physical wound dressing, not an algorithm.

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

• Type of Ground Truth: For biocompatibility, the "ground truth" is established by the results of standardized biological tests performed according to ISO 10993-1. These tests assess endpoints like cytotoxicity, irritation, and sensitization. For substantial equivalence, the "ground truth" is the established safety and effectiveness profile of the predicate devices.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This is a physical wound dressing, not a machine learning model.

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

• Ground Truth for Training Set: Not applicable.

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