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510(k) Data Aggregation
(42 days)
SYNTEX POWDER-FREE LATEX EXAMINATION GLOVES WITH LOW PROTEIN CLAIM
A glove is worn on the hand of healthcare worker and similar personnel to prevent contamination between healthcare personnel and the patient's body, fluids, waste or environment.
Powder-Free Latex Examination Glove with Protein Labeling Claim ( 50 Micrograms or Less )
This document is a 510(k) clearance letter from the FDA for a Powder-Free Latex Examination Glove with Protein Labeling Claim (50 Micrograms or Less).
Unfortunately, this type of document is not relevant to the request of describing acceptance criteria and a study proving a device meets those criteria for an AI/ML medical device.
Here's why:
- Device Type: This document pertains to a physical medical device (examination gloves), not an AI/ML software device.
- Regulatory Pathway: The 510(k) pathway for traditional medical devices primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, often through performance testing on physical attributes (e.g., tensile strength, barrier integrity, protein content for allergies). It does not typically involve the kinds of clinical performance studies (e.g., diagnostic accuracy, sensitivity, specificity) that are foundational to AI/ML device evaluations.
- Missing Information: The letter itself only confirms the clearance and does not detail the specific test methods, acceptance criteria, or study designs used to support the substantial equivalence claim for the glove. It references a "Protein Labeling Claim," implying tests were done for protein content, but no specifics are provided.
Therefore, I cannot extract the requested information (acceptance criteria table, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, training set details) from this document as it simply isn't present or relevant to an AI/ML context.
To provide the requested information, I would need a document that describes the clinical validation or performance study for an AI/ML-based medical device.
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