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510(k) Data Aggregation
(120 days)
POWDER TREE NITRILE EXAMINATION GLOVES, BLUE NON STERILE is a disposable device and make of Synthetic Is a Gropodosatibits rubber like Characteristics Intended For Medical Purpose that is worn on the Incended roz finger to prevent contamination between patient and examiner -
The Powder Free Nitrile Examination Gloves, Blue, Non Sterile meets all the requirements of ASTM standard D 6319-00a53 and FDA 1000 ml Water Leak Test.
The provided document describes the acceptance criteria and performance data for "Powder Free Nitrile Blue Examination Gloves, Blue, Non Sterile." This is a medical device and not an AI/ML powered device, so many of the requested fields are not applicable.
Here's the information extracted from the document:
1. Table of Acceptance Criteria and Reported Device Performance:
| CHARACTERISTICS | STANDARDS | ACCEPTANCE CRITERIA | DEVICE PERFORMANCE |
|---|---|---|---|
| Dimension | D 6319-00aE3 | Meets D 6319-00aE3 | Meets |
| Physical Properties | D 6319-00aE3 | Meets D 6319-00aE3 | Meets |
| Freedom from Pinholes | D 6319-00aE3, FDA 21 CFR 800.20 | Meets D 6319-00aE3, FDA 21 CFR 800.20 | Meets (specifically, FDA 1000 ml Water Leak Test and Pinhole AQL) |
| Powder Residue | D 6319-00aE3, D6124 - 01 | < 2 mg/glove | < 2 mg/glove |
| Biocompatibility | Primary Skin Irritation in Rabbits, Dermal Sensitization | Passes (No primary skin irritation), Passes (No contact sensitizer) | Passes (No primary skin irritation), Passes (No contact sensitizer) |
| Tensile Strength (Before Aging) | ASTM D 6319-00a53 Specifications | 14 MPa, min | Meets |
| Ultimate Elongation (Before Aging) | ASTM D 6319-00a53 Specifications | 500 % min | Meets |
| Tensile Strength (After Aging @ 100°C for 22 hr) | ASTM D 6319-00a53 Specifications | 14 MPa, min | Meets |
| Ultimate Elongation (After Aging @ 100°C for 22 hr) | ASTM D 6319-00a53 Specifications | 400 % min | Meets |
| ASTM Pinhole Requirements | ASTM D 6319-00a53 Specifications | Single, General Inspection Level G-1, AQL 2.5 | Adherence: FULL |
2. Sample size used for the test set and the data provenance:
- Sample Size: The document does not specify a numerical sample size for the various tests. It mentions "Testing, and Device Test Report of Compliance of finished gloves are provided in Attachment D, E and F" which would presumably contain this information, but these attachments are not included in the provided text.
- Data Provenance: The tests were conducted by PT Mandiri Inti Buana, a manufacturer located in SUMUT – INDONESIA. The data appears to be prospective as it is part of a premarket notification for a new device.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This is not applicable as the device is a physical product (examination gloves), and its performance data is based on standardized physical and chemical tests, not expert interpretation of outputs.
4. Adjudication method for the test set:
Not applicable for this type of device. Performance is objectively measured against standards.
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 device is not an AI-powered diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not applicable. This device is not an AI algorithm.
7. The type of ground truth used:
The ground truth used for this device is based on established ASTM (American Society for Testing and Materials) standards (specifically D 6319-00aE3 and D 6124-01) and FDA requirements (specifically the 1000 ml Water Leak Test and 21 CFR 800.20 for freedom from pinholes). These are objective, internationally recognized performance specifications for examination gloves.
8. The sample size for the training set:
Not applicable, as this is a physical product and not a machine learning model.
9. How the ground truth for the training set was established:
Not applicable, as this is a physical product and not a machine learning model.
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