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Prescription:
LUOFUCON® Extra Silver Gelling Fiber Dressing may be used for the management of moderate to heavily exuding chronic and acute wounds as an effective barrier to bacterial penetration of the dressing, including:

Partial thickness burns (second degree);

Diabetic foot ulcers;

Leg ulcers (venous stasis ulcers, arterial ulcers and leg ulcers of mixed etiology);

Pressure ulcers (partial and full thickness);

Donor sites;

Surgical wounds;

Traumatic wounds.

OTC:

LUOFUCON® Silver Antibacterial Gelling Fiber Dressing may be used for: Minor abrasions; Minor lacerations; Minor cuts; Minor scalds and burns.

LUOFUCON® Extra Silver Gelling Fiber Dressing is a soft, conformable, non-woven pad or ribbon dressing composed of carboxymethyl cellulose fibers, high-density polyethylene and Polyethylene terephthalate fibers and 1.1% (w/w) ionic silver. Based on in vitro testing, the silver in the dressing inhibits bacterial growth in the dressing and provides a barrier against bacterial penetration through the dressing for up to seven days. This conformable and highly absorbent dressing absorbs wound fluid and creates a soft gel, provides an ideal moist wound healing environment.

The provided text describes the 510(k) submission for the LUOFUCON® Extra Silver Gelling Fiber Dressing and LUOFUCON® Silver Antibacterial Gelling Fiber Dressing. The submission focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a clinical study.

Therefore, many of the requested details about acceptance criteria, clinical study methodologies, sample sizes, expert involvement, and ground truth establishment are not explicitly provided in this type of regulatory document, which is primarily focused on a comparison to existing devices.

However, based on the information available, here's what can be extracted:

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

The document implicitly uses the performance of the predicate devices as a comparative benchmark. Explicit acceptance criteria in a quantitative sense are mentioned for some 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: Not specified for any of the performance tests.
• Data Provenance: The tests are described as "in vitro testing" and a "porcine full thickness dermal wound healing study." This indicates a mix of lab-based and animal model studies. The country of origin of the data is not specified, but the manufacturer is based in China. The studies are assumed to be prospective as they are conducted for this 510(k) submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

Not applicable. The document describes laboratory and animal studies, not human clinical trials that would typically involve expert-established ground truth.

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

Not applicable. This is not a clinical study involving human readers or interpretation 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:

Not applicable. This is for a wound dressing, not a diagnostic AI device.

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

Not applicable. This is for a wound dressing, not an algorithm.

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

For the in vitro tests, the ground truth is established by the standardized methods themselves (e.g., AATCC TM100 for antibacterial effectiveness). For the porcine study, the ground truth would be based on direct observation of wound healing performance and histopathology, as assessed by the researchers conducting the study.

8. The sample size for the training set:

Not applicable. This is for a wound dressing, not a machine learning model.

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

Not applicable. This is for a wound dressing, not a machine learning model.

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