Search Results

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.

Ask a specific question about this device