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Sorbact® Ribbon Gauze is intended for use in the management of exuding partial to full thickness wounds (including clean, colonized, contaminated or infected wounds). Sorbact® Ribbon Gauze is indicated for shallow cavity wounds and fistulas.

Sorbact® Ribbon Gauze is a sterile (gamma irradiation), single use only, hydrophobic microbe binding wound dressing. It consists of a Sorbact® wound contact layer, which allows passage of wound exudate into a secondary dressing.

Based on the provided text, the device in question is the "Sorbact® Wound Dressing – Ribbon Gauze". However, this document is a 510(k) premarket notification for a modification to an existing device, specifically a change to the color additive. The provided information does not describe a study that uses a test set to prove the device meets performance criteria related to its clinical efficacy or diagnostic performance as an AI/ML device.

The performance data section explicitly states: "The modification addressed by this 510(k) is a change to the color additive in the Sorbact® Ribbon Gauze. As the intended use, device description, wound contact material, instructions for use, mechanism of action, storage conditions, and shelf life of the modified device are the same as that of the predicate device, both devices have the same fundamental scientific technology."

Therefore, the "performance data" presented is entirely focused on non-clinical testing related to the safety and material properties of the new color additive, rather than a clinical performance study involving a test set, ground truth, or human readers, as would be expected for an AI/ML device.

Given this, I cannot fill in the requested table and answer the specific questions (2-9) because the information is not present in the provided document. The document describes a traditional medical device (wound dressing) and a 510(k) submission for a minor change (color additive), not an AI/ML device or its performance evaluation in the context you've outlined.

Here's what I can extract regarding acceptance criteria and performance, as related to the specific modification:

1. Table of Acceptance Criteria and Reported Device Performance (for the color additive change):

Missing Information (as per your prompt's format, because it's not an AI/ML device):

2. Sample sizes used for the test set and data provenance: Not applicable. This was non-clinical material and biocompatibility testing, not a clinical test set.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No clinical ground truth established.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
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.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. Safety and material testing, not clinical diagnosis.
8. The sample size for the training set: Not applicable. No AI/ML training set.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document pertains to a regulatory submission for a minor modification to a non-AI/ML medical device. It does not contain the information required to address your specific questions about AI/ML device performance studies.

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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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OTC: Sorbact Gel Wound Dressing is intended for use in the management of minor wounds (dry to low exuding) and to attract water repelling germs. The dressing may be used on minor scrapes, cuts, sores and burns.

Rx: Sorbact Gel Wound Dressing is intended for use in the management of dry to low exuding partial to full thickness wounds (including clean, colonized, contaminated or infected wounds). The dressing is indicated for post-operative wounds, trauma wounds, shallow cavity wounds, fistulas, pressure ulcers, diabetic ulcers, and venous ulcers.

Sorbact® gel Wound Dressings are sterile bandages that attract and capture water-repelling (hydrophobic) microorganisms.

The provided 510(k) summary for the Sorbact® Gel Wound Dressing (K100591) does not contain information about a study proving the device meets specific acceptance criteria in the manner typically described for AI/ML-based medical devices (e.g., performance metrics like sensitivity, specificity, or AUC against a ground truth). Instead, the summary focuses on demonstrating substantial equivalence to a predicate device.

Therefore, many of the requested elements for a detailed study description are not applicable or cannot be extracted from this document. However, I can provide the available information based on the content.

Here's an analysis of the "acceptance criteria" and the "study" as presented in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are implicitly based on demonstrating that the new device, Sorbact® gel, performs equivalently to the predicate device, Sorbact® Wound Dressing, in its ability to bind hydrophobic microorganisms, and also passes standard biocompatibility tests.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not specified for the comparative microbial binding test. For biocompatibility, the number of samples/subjects is not mentioned, but these are typically standardized in vitro or in vivo tests.
• Data Provenance: Not specified. Standard biocompatibility tests follow international guidelines (e.g., ISO 10993). The microbial binding test is a comparative test performed by the manufacturer. Whether it involved human data, animal data, or in vitro models, or its geographic origin, is not stated.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications

• Not applicable. This study does not involve establishing ground truth from expert readings for image analysis or diagnostic performance, as it is a wound dressing. The "ground truth" for microbial binding is the performance of the predicate device. For biocompatibility, it's adherence to established safety standards.

4. Adjudication Method for the Test Set

• Not applicable. There is no mention of an adjudication method, as it's not a reader study.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• No. This is not an MRMC study. It is a comparison of a modified medical device to its predicate.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Not applicable, as this is not an AI/ML device. The performance claimed is for the device's inherent physical and chemical properties (microbial binding, biocompatibility), not an algorithm.

7. The Type of Ground Truth Used

• Comparative Performance: For microbial binding, the "ground truth" was established by the performance of the legally marketed predicate device, Sorbact® Wound Dressing, in the identical test ("Results of the test were identical for both devices.").
• Safety Standards: For biocompatibility (Skin Sensitization, Cytotoxicity, Skin Irritation), the ground truth is implicitly defined by the pass/fail criteria of recognized international standards (e.g., ISO 10993 series for biological evaluation of medical devices).

8. The Sample Size for the Training Set

• Not applicable. This device does not use a training set in the context of AI/ML.

9. How the Ground Truth for the Training Set Was Established

• Not applicable. This device does not use a training set in the context of AI/ML.

In summary, this 510(k) pertains to a wound dressing and demonstrates substantial equivalence through direct comparison of a specific performance characteristic (microbial binding) and adherence to general safety standards (biocompatibility). It does not involve complex clinical studies or AI/ML performance evaluations typically associated with the detailed questions on acceptance criteria for diagnostic devices.

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Rx: Sorbact® Wound Dressings are intended for use in the management of moderate to heavily exudating partial to full thickness wounds (including clean, colonized, contaminated or infected wounds) and to bind hydrophobic microbes The dressing is indicated for post-operative wounds, trauma wounds, shallow cavity wounds, fistulas, pressure ulcers, diabetic ulcers, and venous ulcers.
OTC: Sorbact® Wound Dressings are intended for use in the management of draining minor wounds and to attract and bind water repelling germs. The dressing may be used on minor scrapes, cuts, sores and burns.

Sorbact Wound Dressings are sterile bandages that attract and capture water-repelling (hydrophobic) microorganisms. The dressings are coated with DACC (dialky) carbamoyl chloride), a hydrophobic (water-repelling) fatty ester acid that binds to and reduces the overall concentration of hydrophobic microbes in a wound each time the dressing is changed. Available Sorbact products include adsorbent dressings, compresses, ribbon gauze tamponades, round swabs and surgical dressings.

The provided text describes the Sorbact® Wound Dressing and its substantial equivalence to predicate devices, but it does not contain acceptance criteria or detailed study results that prove the device meets specific acceptance criteria.

The document states:

• "Sorbact® wound dressings have been shown in studies, including in vitro and clinical tests, to be safe and effective for their intended uses." (Page 4, Section V)

However, it lacks the specific data points needed to answer your questions within the context of acceptance criteria and proven performance. This document is a 510(k) summary, which typically focuses on demonstrating substantial equivalence rather than presenting comprehensive study data with acceptance criteria.

Therefore, I cannot populate the table or provide detailed answers to most of your questions based solely on the provided text.

Here's a breakdown of what can be inferred from the provided text and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Cannot be provided. The document states that in vitro and clinical tests were conducted to show safety and effectiveness, but it does not specify any quantitative acceptance criteria (e.g., "bacterial reduction must be >X%") or the actual performance metrics achieved in those tests.

2. Sample Size Used for the Test Set and Data Provenance

Cannot be provided. The document mentions "clinical tests" but does not provide any details about the sample size (number of patients/wounds), study design, or data provenance (e.g., country of origin, retrospective/prospective nature).

3. Number of Experts Used to Establish Ground Truth and Qualifications

Cannot be provided. The document does not describe the establishment of a ground truth for a test set, nor does it mention any expert involvement in such a process. This kind of detail is typically relevant for studies involving subjective assessments (e.g., image-based diagnostics) which is not the primary focus of a wound dressing's efficacy where clinical outcome is key.

4. Adjudication Method for the Test Set

Cannot be provided. As there's no mention of a test set with subjective assessments by experts, there's no description of an adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Cannot be provided. The document does not describe an MRMC study or any scenario where human readers would improve with or without AI assistance, as this device is a wound dressing, not an AI diagnostic tool.

6. Standalone Algorithm Performance

Cannot be provided. This question is irrelevant as the Sorbact® Wound Dressing is a physical medical device, not an algorithm.

7. Type of Ground Truth Used

Cannot be provided in detail. For a medical device like a wound dressing, the "ground truth" for demonstrating safety and effectiveness would typically be clinical outcomes (e.g., wound healing rates, reduction in infection, absence of adverse events), and potentially microbiological data for its antimicrobial claims. However, the document does not specify how this ground truth was established or measured in the "clinical tests."

8. Sample Size for the Training Set

Cannot be provided. This question is irrelevant as the Sorbact® Wound Dressing is a physical medical device, not a machine learning model that requires a training set.

9. How the Ground Truth for the Training Set Was Established

Cannot be provided. This question is irrelevant for the same reason as point 8.

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