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Next Science Wound Gel (Rx) is indicated for the management of wounds such as Stage I-IV pressure ulcers, partial and full thickness wounds, diabetic foot and leg ulcers, post-surgical wounds, first and second degree burns, grafted and donor sites.

Next Science Wound Gel is a hydrogel that helps to maintain a moist wound environment that is conducive to healing. The antimicrobial agent, benzalkonium chloride, inhibits the growth of microorganisms in the hydrogel. The Wound Gel is applied directly to the wound and then covered with an appropriate dressing. The use of the hydrogel on a wound creates a moist environment that is conducive to healing by either absorbing wound exudate or donating moisture. The Wound Gel will be supplied in both 1 ounce and 4-ounce low-density polyethylene tubes with a screw-top opening. Lot number and expiration dating will be embossed on the printed tube.

The provided text describes a 510(k) premarket notification for a medical device called "Next Science Wound Gel (Rx)". This document focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a clinical study with an AI component.

Therefore, many of the requested details, such as:

• A table of acceptance criteria and reported device performance (in the context of AI)
• Sample sizes for test sets (for AI)
• Number of experts and their qualifications (for AI ground truth)
• Adjudication method (for AI ground truth)
• MRMC comparative effectiveness study results (AI assistance)
• Standalone performance (AI algorithm only)
• Type of ground truth used (for AI)
• Sample size for training set (for AI)
• How ground truth for training set was established (for AI)

are not applicable or available in this document. The document concerns a physical medical device (wound gel) and its biological and functional performance, not an AI/software device.

However, I can extract the information relevant to the device's performance testing and the grounds for its substantial equivalence.

Here's what can be extracted:

1. Acceptance Criteria and Reported Device Performance (as related to the physical device properties):

The document doesn't present a table of quantitative acceptance criteria for biological or clinical performance in the typical sense of an AI/diagnostic study, but rather demonstrates compliance with safety and effectiveness standards through various tests and comparison to predicates.

2. Sample Sample(s) Used for the Test Set and Data Provenance:

• Test Set (for the gel's performance): The document mentions a "full thickness wound study" and "biocompatibility studies" and "USP Antimicrobial Effectiveness Testing and ".
  • Sample Size: Not specified for any of these studies.
  • Data Provenance: Not specified (e.g., country of origin). The studies appear to be laboratory or preclinical studies demonstrating functional and safety aspects of the gel, not clinical trials with human patient data. These would be considered prospective data generation for the purpose of demonstrating device characteristics.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

• Not applicable: This document describes a physical medical device (wound gel) and its chemical/biological properties and performance studies (biocompatibility, antimicrobial effectiveness, wound healing observation), not an AI/software device requiring expert human readers to establish ground truth for image interpretation or similar.

4. Adjudication Method for the Test Set:

• Not applicable: As above, this is not an AI/diagnostic study scenario where human adjudication of interpretations is required.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

• No: This is not relevant to the presented device approval (wound gel). There is no AI component mentioned in this document.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done:

• No: This is not relevant to the presented device approval (wound gel). There is no AI component mentioned in this document.

7. The Type of Ground Truth Used:

• For the physical properties and performance of the gel:
  • Biocompatibility: Likely based on established ISO 10993 standards and assays, with "ground truth" being the measured biological reaction (e.g., cytotoxicity, irritation) against control limits.
  • Functional Performance (Wound Study): Likely observational data on wound healing, potentially compared to controls or predicate devices.
  • Preservative Efficacy: Based on quantitative microbial reduction/inhibition as defined by USP standards.

8. The Sample Size for the Training Set:

• Not applicable: This is a physical medical device, not an AI/machine learning model that requires a training set.

9. How the Ground Truth for the Training Set was Established:

• Not applicable: This is a physical medical device, not an AI/machine learning model that requires a training set.

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Next Science™ Wound Gel is indicated for the management of skin abrasions, minor irritations, cuts, exit sites and intact skin.

The Next Science™ Wound Gel is a white, virtually odorless hydrogel. The Wound Gel provides management of skin abrasions, lacerations, minor irritations, cuts, exit sites, and intact skin by maintaining a moist wound environment that is conducive to wound healing while inhibiting the growth of microorganisms in the hydrogel. The Wound Gel is applied directly to the wound and then covered with an appropriate dressing. The use of the hydrogel on a wound creates a moist environment that is conducive to wound healing. The Wound Gel will be supplied in both 1 ounce and 4-ounce low-density polyethylene tubes with a screw-top opening. Lot number and expiration dating will be embossed on the printed tube.

The provided document is a 510(k) summary for the Next Science™ Wound Gel. It describes the device, its intended use, and its substantial equivalence to predicate devices, supported by performance testing. However, it does not contain specific acceptance criteria, detailed study results, sample sizes for test or training sets, expert qualifications, or adjudication methods for an AI/CAD/software device as requested in the prompt. The "performance testing" mentioned is chemical/biological for a wound gel, not algorithm performance.

Therefore, many of the requested fields cannot be filled based on the provided text.

Here's what can be extracted and what cannot:

1. Table of acceptance criteria and reported device performance:

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

• Sample Size (Test Set): Not specified in the document. The studies mentioned are a "full thickness wound study" and "USP Antimicrobial Effectiveness Testing prototypes/samples."
• Data Provenance: Not specified.

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

• Not applicable/Not specified. This relates to diagnostic interpretation, which is not the function of this wound gel.

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

• Not applicable/Not specified. This relates to diagnostic interpretation.

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:

• No. This is a wound gel, not an AI/CAD device.

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

• No. This is a wound gel, not an AI/CAD device.

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

• For biocompatibility: Adherence to ISO standards, implying laboratory testing against specific metrics.
• For wound management: Results from a "full thickness wound study," which would likely involve direct observation of healing, possibly histological assessment, or other direct biological measurements.
• For preservative efficacy: Results against USP and standards, which are laboratory microbiological tests.

8. The sample size for the training set:

• Not applicable/Not specified. This product is a wound gel, not a machine learning model.

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

• Not applicable/Not specified. This product is a wound gel, not a machine learning model.

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