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This product manages the pain in all types of oral wounds, mouth sores, injuries and ulcers of the oral mucosa. It adheres to oral tissue and forms a protective barrier between the wound and further irritation and contamination. It provides the moist wound environment required for optimal wound healing. Examples of oral lesions include canker sores (aphthous ulcers), tooth extraction sites, oral mucositis, oral stomatic ulcers such is caused by braces and dentures, and irritation and pain following deep tooth scaling.

"Sock It!" Oral Pain Gel is a clear, viscous hydrogel wound dressing composed entirely of foodgrade ingredients including a food-grade preservative system. It is safe if swallowed, is designed to be physiologically compatible with both intact and compromised tissue in the mouth, and will manage the pain associated with all types of injuries to the mouth. The gel's primary mode of action for pain relief is that it adheres to the wound surface, conforms to the contours of the wound, and protects the wound from contamination and irritation by forming a protective barrier that is similar to the natural mucosa. It also creates and maintains a moist wound environment, which is necessary for the natural healing process. The pH and osmotic pressure of the gel are adjusted to be compatible with saliva.

The provided 510(k) summary for "Sock It!" Oral Pain Gel does not present explicit acceptance criteria or a dedicated study demonstrating the device's performance against such criteria in the manner typically seen in AI/ML device submissions. This document is a premarket notification for a medical device that was submitted in 2006, prior to the widespread adoption of AI/ML in medical devices and the associated regulatory expectations for performance studies.

Instead, the submission relies on the concept of substantial equivalence to predicate devices. The primary assertion is that "Sock It!" Oral Pain Gel is substantially equivalent to existing legally marketed hydrogel oral wound dressings based on its intended use, technological characteristics, and safety profile.

Here's an analysis based on the information provided, formatted to address your questions where possible, and noting where information is not available due to the nature of this type of submission:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: The document does not define specific, measurable acceptance criteria in terms of clinical efficacy or quantitative performance metrics. Its acceptance by the FDA is based on demonstrating that it is substantially equivalent to predicate devices that are already on the market. This framework implies that if the new device has the same intended use and similar technological characteristics to a legally marketed device, and does not raise new questions of safety or effectiveness, it can be marketed.

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

• Sample Size for Test Set: Not applicable / Not provided. This submission does not describe a clinical performance study with a distinct "test set" as understood in AI/ML evaluations. The equivalency claim is based on the device's formulation and mechanism of action being similar to predicate devices.
• Data Provenance: Not applicable. There is no specific data provenance described for a performance study.

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

• Not applicable. There was no explicit "ground truth" establishment for a test set in the context of a performance study described in this 510(k) summary. The "ground truth" for the predicate devices' effectiveness would have been established through their initial regulatory pathways and clinical use.

4. Adjudication Method for the Test Set

• Not applicable. No performance study with adjudication is described.

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

• Not applicable. This is not an AI/ML device, and no MRMC study is mentioned. The submission focuses on substantial equivalence, not on comparative effectiveness with human readers or AI assistance.

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

• Not applicable. This is a physical hydrogel product, not an algorithm.

7. The Type of Ground Truth Used

• Type of "Ground Truth" (Implicit): The "ground truth" here is implicitly derived from the established safety and effectiveness of the existing predicate devices through their prior regulatory clearances and clinical experience. The claim is that "Sock It!" offers the same performance (pain management, protective barrier, moist environment) as these already approved devices due to its similar mechanism and composition.

8. The Sample Size for the Training Set

• Not applicable. This is not an AI/ML device that requires a training set. The development likely involved formulation and in-vitro testing to ensure physical properties and safety, but not an AI-specific training set.

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

• Not applicable. As there's no AI/ML training set, there's no ground truth establishment for it.

Summary of the Study (or lack thereof) Proving Acceptance:

The submission for "Sock It!" Oral Pain Gel does not outline a specific study designed to meet quantitative acceptance criteria. Instead, the "proof" the device meets acceptance criteria ("intended use" and "technological characteristics") is established through the argument of substantial equivalence to already approved predicate devices.

The submission highlights:

• Intended Use: The indications for use are "the same as these for the predicate devices."
• Technological Characteristics: The device is a hydrogel wound dressing, similar to predicate devices, and its primary mode of action (adhering, forming a protective barrier, creating a moist environment) is the same. The only difference noted is the method of hydration (factory-hydrated vs. user-hydrated or saliva-hydrated). The submission argues this difference is "non-substantial" and does not alter the fundamental safety or effectiveness.

Therefore, the "study" demonstrating acceptance is essentially the comparison to predicate devices and the argument that the differences do not introduce new questions of safety or effectiveness. The FDA's issuance of the 510(k) clearance confirms their agreement that substantial equivalence was demonstrated.

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BIONECT Hydrogel, Gauze Pads, Foam, Spray, and Clear Hydrogel are indicated for the dressing and munagement of minor burns: superficial cuts, lacerations, and abrasions; and minor irritations of the skin. A health care professional may be consulted prior to the first use of these products to delermine whether these conditions exist. BIONECT Hydrogel, Gauze Pads, Fourn, Spray, and Clear Hydrogel may also be used under the care of a health care professional for wounds such as partial to full thickness dermal ulcers (pressure ulcers, venous stasis ulcers, arterial ulcers, diabetic ulcers), surgical wounds (post-operative incisions and donor sites), and second degree burns.

BIONECT Hydrogel is propared from sodium hyaluronate and purified water to form a hydrogel dressing. Sodium hyaluronate is a polysaccharide biopolymer that can increase the viscosity of the gel. BIONECT® Hydrogel was designed to provide a soothing, moist environment that is supportive to wound healing.

The provided text describes the BIONECT® Hydrogel, and its substantial equivalence to predicate devices, rather than a study with specific acceptance criteria and detailed performance metrics. The information focuses on regulatory approval (510(k) summary) and intended use. Therefore, a direct answer to the request for acceptance criteria and a study proving device performance as typically expected from a clinical trial with statistical outcomes is not fully available in the provided text.

Based on the provided text, here's an attempt to answer the questions, highlighting what information is present and what is missing:

1. Table of acceptance criteria and the reported device performance

The provided document is a 510(k) summary and not a clinical study report. It focuses on demonstrating "substantial equivalence" of BIONECT® Hydrogel to already marketed predicate devices based on technological characteristics and intended use. Therefore, explicit, quantitatively defined "acceptance criteria" and direct, statistically reported "device performance" in the sense of a new clinical trial proving efficacy against a predefined benchmark are not presented. The "performance" is primarily described in terms of biocompatibility and safety, and by claiming equivalence to predicate devices.

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

• Test Set (for biocompatibility): Not explicitly stated. The document mentions "in vitro and in vivo biocompatibility testing" but doesn't provide specific sample sizes for these tests.
• Test Set (for clinical experience): "approximately 600 patients with various types of ulcers, burns, and surgical wounds" across "21 clinical trials (18 controlled)".
• Data Provenance: Europe (for the clinical experience cited). The text does not specify retrospective or prospective. Given they are "clinical trials," they are typically prospective, but this is not explicitly stated.

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

The document does not provide this information. The clinical experience cited (from Europe) is referred to generally in support of safety, not as a ground-truthed test set for a specific AI or diagnostic device.

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

The document does not provide this information.

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 such study is mentioned or implied. This device (BIONECT® Hydrogel) is a wound dressing, not an AI-assisted diagnostic or imaging device that would typically involve human readers.

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

Not applicable. BIONECT® Hydrogel is a physical wound dressing, not an algorithm.

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

For the biocompatibility tests, the ground truth would be established by the biological responses observed in the in vitro and in vivo models according to established standards. For the clinical experience (cited for safety), "outcomes data" related to safety (e.g., adverse events) would be the ground truth, though details aren't provided.

8. The sample size for the training set

Not applicable. This is a medical device (wound dressing), not a machine learning model, so there is no "training set."

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

Not applicable.

Summary of Limitations from the Text:

The provided text is a 510(k) Premarket Notification summary for a wound dressing. Its purpose is to demonstrate substantial equivalence to existing devices for regulatory approval, primarily based on safety and similar technological characteristics, not to present a detailed clinical study with performance criteria for a novel device or AI. Therefore, most of the specific questions related to acceptance criteria, sample sizes for test/training sets, expert involvement, and advanced study designs (like MRMC) as commonly discussed for AI/diagnostic devices are not addressed in this type of document.

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BIONECT® Cream is indicated for the dressing and management of partial to full thickness dermal ulcers (pressure sores, venous stasis ulcers, arterial ulcers, diabetic ulcers), wounds including cuts, abrasions, donor sites, and post-operative incisions, irritations of the skin, and first and second degree burns. The dressing is intended to cover a wound or burn on a patient's skin, and protect against abrasion, friction, and desiccation.

BIONECT® Cream acts to provide a moist wound environment and protect the wound. BIONECT® Cream was designed to provide a soothing, moist environment for easy application to various types of wounds as a hydrogel dressing. BIONECT® Cream is prepared from sodium hyaluronate and purified water to form a hydrogel dressing. Sodium hyaluronate is a polysaccharide biopolymer that can increase the viscosity of the gel. BIONECT® Cream was designed to provide a moist wound healing environment.

The provided text describes a medical device called BIONECT® Cream, a hyaluronic acid sodium salt hydrogel dressing. It mainly focuses on its description, intended use, and comparison to predicate devices, rather than a detailed study with acceptance criteria often seen for AI/ML devices.

Therefore, many of the requested sections (e.g., sample size for test set, number of experts, adjudication methods, MRMC studies, standalone performance, training set details) are not applicable (N/A) based on the provided document as they relate to performance evaluation methodologies for AI/ML or complex diagnostic devices, which BIONECT® Cream is not.

Here's the information that can be extracted from the provided text:

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

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: Approximately 600 patients.
• Data provenance: Conducted in Europe across 21 clinical trials (18 controlled). The text does not explicitly state if the trials were retrospective or prospective, but clinical trials are generally prospective.

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)

• N/A. The assessment relies on clinical trials and biocompatibility testing, not on establishing ground truth via expert consensus in the way an AI/ML diagnostic would for a test set. The "experts" involve the clinicians conducting the trials, but their roles in "establishing ground truth" for a specific test set, as defined for AI/ML, are not applicable here.

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

• N/A. This concept is specific to resolving discrepancies in expert labeling or diagnoses for test sets, which is not relevant to the evaluation of a wound dressing.

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

• N/A. This type of study is relevant for AI-assisted diagnostic devices, not for a wound dressing.

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

• N/A. This is a wound dressing, not an algorithm.

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

• The "ground truth" for safety and intended use was established through:
  • Biocompatibility testing: Standardized tests demonstrating material safety.
  • Clinical outcomes data: Observation of patient outcomes and safety profiles in clinical trials involving approximately 600 patients with various wounds and burns.

8. The sample size for the training set

• N/A. This device does not involve a "training set" in the context of machine learning.

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

• N/A. This device does not involve a "training set" in the context of machine learning.

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