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510(k) Data Aggregation
(114 days)
SENSTOUCH HIGH RISK LATEX EXAMINATION GLOVES, POWDER FREE, NON STERILE
High Risk Latex Examination Gloves, Powder Free Non Sterile is a disposable device and made of natural rubber latex for medical purpose that is worn on the examiner's hand or finger to prevent conta mination between patient and examiner.
HIGH RISK LATEX EXAMINATION GLOVES, POWDER FREE NON STERILE is a disposable device and made of natural rubber latex.
This document is about the FDA's 510(k) premarket notification for "High Risk Latex Examination Gloves, Powder Free, Non Sterile". It's a regulatory clearance document, not a study report for a novel medical device with AI components. Therefore, much of the information requested in the prompt (e.g., acceptance criteria for an algorithm, sample sizes for training/test sets, expert qualifications, MRMC studies) is not applicable or present in this type of document.
However, I can extract the relevant information about the "device" in question (gloves) and its regulatory classification.
Here's a breakdown of what can be inferred or directly stated from the provided text, and what cannot:
1. A table of acceptance criteria and the reported device performance
Acceptance Criteria (for gloves) | Reported Device Performance (implied by FDA clearance) |
---|---|
Substantially equivalent to legally marketed predicate devices | Passed FDA's 510(k) review for substantial equivalence |
Meets general controls and provisions of the Federal Food, Drug, and Cosmetic Act (Act) | Complies with general controls (annual registration, listing, good manufacturing practice, labeling, prohibitions against misbranding and adulteration) |
Intended purpose: prevent contamination between patient and examiner | Suitable for medical purpose, worn on examiner's hand/finger to prevent contamination |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Not applicable / Not provided. This document does not detail specific testing protocols or sample sizes for performance evaluation in the context of an AI/algorithm-driven device. For gloves, testing typically involves standards related to physical properties (e.g., tensile strength, barrier integrity, dimensions) and biocompatibility, but the specifics are not in this FDA letter. The device manufacturer (Pt. Mahakarya Inti Buana) is from Sumut, Indonesia.
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 / Not provided. This information is for evaluating AI or diagnostic device performance, not for gloves.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable / Not provided. This pertains to expert review in AI studies.
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 / Not provided. This is relevant for diagnostic devices that involve human interpretation assisted by AI.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable / Not provided. There is no algorithm described for this device.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
- Not applicable / Not provided. The "ground truth" for gloves would relate to their physical and biological properties meeting established standards (e.g., ASTM standards for medical gloves) and regulatory requirements, rather than clinical outcomes or diagnostic accuracy.
8. The sample size for the training set
- Not applicable / Not provided. There is no AI model or "training set" for this device.
9. How the ground truth for the training set was established
- Not applicable / Not provided. There is no AI model or "training set" for this device.
In summary, the provided document is a regulatory clearance for medical gloves, which are a physical product, not a software or AI-driven diagnostic device. Therefore, the specific questions regarding AI device performance evaluation, expert reviews, and data sets for algorithms are not relevant to this document.
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