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Rx Indications for Use: PluroGel® Antimicrobial Burn and Wound Dressing is indicated for the management of full and partial thickness wounds including surgically created wounds, dermal ulcers (pressure ulcers), leg ulcers (diabetic foot ulcers), superficial wounds, first degree burns, partial thickness burns, and donor sites. PluroGel® Antimicrobial Burn and Wound Dressing maintains a moist wound and skin environment that supports autolytic debridement of necrotic tissue.

OTC Indications for Use: PluroGel® Burn and Wound Dressing (OTC) is indicated for the management of minor cuts, minor burns, and minor lacerations. PluroGel® Burn and Wound Dressing (OTC) maintains a moist wound and skin environment.

PluroGel® Antimicrobial Burn and Wound Dressing and PluroGel® Burn and Wound Dressing (OTC) is a translucent, biocompatible, odorless, semisolid gel intended to provide and maintain a clean and moist wound environment that is conducive to healing. The dressing maintains moisture in the wound and protects the wound from desiccation to provide an optimal moist wound environment conducive to wound healing. PluroGel® Antimicrobial Burn and Wound Dressing and PluroGel® Burn and Wound Dressing (OTC) is non-cytotoxic, contains a surfactant, and is water soluble to aid in the removal of wound debris in between dressing changes.

PluroGel® is provided non-sterile and contains phenoxyethanol and potassium sorbate as antimicrobial preservative agents to inhibit the growth of microorganisms within the product during shelf storage before and after opening.

I am sorry, but the provided text does not contain the information required to answer your request regarding the acceptance criteria and the study that proves the device meets the acceptance criteria for a medical device that uses AI. The document discusses a 510(k) premarket notification for "PluroGel® Antimicrobial Burn and Wound Dressing" and "PluroGel® Burn and Wound Dressing (OTC)," which are wound dressings and not AI-powered devices.

Therefore, I cannot extract details about:

• A table of acceptance criteria and reported device performance for an AI device.
• Sample sizes for test sets, data provenance, or details about training sets for an AI model.
• Number of experts, their qualifications, or adjudication methods for ground truth establishment in an AI context.
• MRMC studies, effect sizes for human readers with AI assistance, or standalone algorithm performance.
• The type of ground truth (expert consensus, pathology, outcomes data) relevant to AI.

The document primarily focuses on the substantial equivalence of the wound dressing product to a predicate device based on its intended use, technological characteristics (ingredients, form, sterility), and non-clinical testing (biocompatibility, shelf life, period of use after opening, and a single animal wound healing study). It explicitly states "Summary of Clinical Testing: Not applicable," which further confirms no human clinical trials (nor AI performance studies) were conducted for this submission.

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The MEBO Wound Dressing is indicated for management of the following types of wound:-Skin graft recipient sites Newly sutured wounds Lacerations and abrasions Minor or superficial-partial thickness burns

The MEBO Wound Dressing is a CO60 gamma-radiation sterilized dressing consisting of sesame oil, beeswax and fabric dressing, which provides a moist environment for wound healing.

The provided text describes a 510(k) premarket notification for a medical device called "MEBO Wound Dressing." It focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific performance acceptance criteria for a new clinical claim. Therefore, much of the requested information about performance testing, ground truth, expert opinions, and comparative effectiveness studies is not present in the provided document.

The document discusses various tests for the wound dressing (e.g., appearance, size deviation, liquid absorbency, biocompatibility), but these are primarily for substantiating equivalence to the predicate device, not for establishing a new clinical performance claim with defined acceptance criteria for efficacy or diagnostic accuracy.

Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is missing:

Acceptance Criteria and Device Performance (Based on Substantial Equivalence)

The document doesn't define "acceptance criteria" in the sense of predefined thresholds for clinical performance (e.g., sensitivity, specificity, or wound healing rates) that a novel device needs to meet. Instead, it aims to show substantial equivalence to a predicate device. The performance testing conducted is to demonstrate that the new device performs similarly in critical characteristics to the predicate, implying it is equally safe and effective.

Table of Acceptance Criteria and Reported Device Performance:

Note: The document states "The bench testing performed verifies that the performance of the subject device is substantially equivalent in terms of critical performance characteristics to the predicate device." It does not provide specific numerical results or detailed comparison tables for each test, only that they were completed and found to meet the equivalence claim.

Study Details

The provided text describes bench testing to support a 510(k) submission for substantial equivalence, not a study proving clinical efficacy or diagnostic accuracy of an AI/algorithm-based device. Therefore, many of the requested points are not applicable or not detailed in this type of submission.

1. Sample sizes used for the test set and data provenance:

   • Not specified for any performance tests. The document refers to "bench testing" but does not detail sample sizes for physical or biological tests.
   • Data Provenance: Not applicable in the context of clinical data for AI testing; tests are laboratory-based.

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

   • Not Applicable: This document is for a wound dressing, not an AI/algorithm-based device requiring expert interpretation for ground truth. The "ground truth" for the characteristics tested (e.g., size, pH, cytotoxicity) would be established by standard laboratory measurement techniques and expert interpretation of those lab results (e.g., pathologist for biocompatibility slides), but specific numbers/qualifications of these experts are not provided as they would be for image-based AI studies.

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

   • Not Applicable: This pertains to establishing ground truth in expert-read data (e.g., radiology images), which is not the subject of this 510(k).

4. 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 is an MRMC study and applies to AI/CADe/CADx devices, not a wound dressing. No human readers or AI assistance are involved in the performance testing described.

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

   • Not Applicable: This is for AI/algorithm performance. The device is a physical wound dressing composed of sesame oil, beeswax, and fabric.

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

   • For the physical and chemical properties (e.g., size, absorbency, acid value, paste content, B-sitosterol content), the "ground truth" is established by standard analytical and quantitative laboratory methods (e.g., gravimetry, titration, chemical analysis).
   • For biocompatibility, the "ground truth" is established by standardized biological assays as per ISO 10993 series and USP monographs (e.g., cell culture for cytotoxicity, animal models for irritation/sensitization/systemic toxicity, bacterial tests for endotoxins/pyrogens), with expert interpretation of results (e.g., by toxicologists or pathologists for tissue reactions).

7. The sample size for the training set:

   • Not Applicable: This refers to machine learning/AI models. The device is not an AI algorithm; it's a physical wound dressing.

8. How the ground truth for the training set was established:

   • Not Applicable: As above, this document describes a physical medical device, not an AI system.

Summary of Document's Purpose:

The provided document is a 510(k) clearance letter and summary for a wound dressing, demonstrating its substantial equivalence to a legally marketed predicate device. The "study" referenced is a series of bench tests (physical, chemical, and biological/biocompatibility) designed to show that the MEBO Wound Dressing performs equivalently to the predicate and raises no new questions of safety or effectiveness. It is not a clinical trial or performance study for a novel diagnostic or AI device with specific clinical performance acceptance criteria (like sensitivity/specificity).

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