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Rx.: Sorbact® Foam Gentle Border is inthe management of moderately exuding partial to full thickness wounds (including clean, colonized, or infected wounds): chronic wounds (venous and arterial ulcers, diabetic ulcers and pressure ulcers), postoperative dehisced wounds and traumatic wounds.

Rx.: Sorbact® Superabsorbent is indicated for use in the management of heavily partial to full thickness wounds (including clean, colonized, contaminated, or infected wounds (venous and arterial ulcers, diabetic ulcers and pressure ulcers), postoperative dehisced wounds and traumatic wounds.

Sorbact® Absorbent Dressings come in two models, Sorbact® Foam Gentle Border and Sorbact® Superabsorbent, for use with moderately or heavily exuding wounds, respectively. Both dressings come in multiple sizes, are sterile (EO), hydrophobic microbe binding, and for single use only. The dressings combine a Sorbact® wound contact layer with absorbent polyurethane foam or a superabsorbent core. The dressings are covered by a semi-permeable polyurethane film or polypropylene non-woven. A fixation border is made of silicone adhesive.

The provided text describes a 510(k) premarket notification for a medical device, specifically Sorbact® Absorbent Dressings (Sorbact® Foam Gentle Border, Sorbact® Superabsorbent). The document focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study proving that the device meets specific acceptance criteria in the context of AI/algorithm performance.

Therefore, many of the requested sections about acceptance criteria, study details, human reader performance, ground truth, and training sets are not applicable to this document as it pertains to a traditional medical device (wound dressing) submission, not an AI/ML-driven diagnostic or assistive device.

However, I can extract the information provided regarding the device's performance, which is primarily focused on bench testing and biocompatibility.

Here's a breakdown of the requested information based on the provided text:

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

Since this is not an AI/ML device, the concept of "acceptance criteria" for algorithmic performance (e.g., sensitivity, specificity) is not directly applicable. The device's performance is demonstrated through various verification and validation tests as part of its substantial equivalence claim.

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 individual bench or biocompatibility tests. The document states "representative samples" were used.
• Data Provenance: Not specified, but generally refers to in vitro lab testing for this type of device.

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. Ground truth as typically defined for AI/ML performance (e.g., expert consensus on clinical findings) is not relevant for wound dressings. Bench tests and biocompatibility evaluations follow standardized protocols.

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

Not applicable. Adjudication methods are specific to subjective assessments, typically in clinical studies or image interpretation. These tests are objective laboratory evaluations.

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 a wound dressing, not an AI-assisted diagnostic or interpretation tool.

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

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

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

Not applicable. The "ground truth" for the performance of this device is based on established scientific principles and standardized testing methodologies for material properties, fluid absorption, sterility, and biocompatibility, as opposed to clinical "ground truth" established by experts or pathology for diagnostic purposes.

8. The sample size for the training set

Not applicable. The concept of a "training set" is for AI/ML algorithms. This is a physical medical device.

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

Not applicable, as there is no training set for this device.

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