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510(k) Data Aggregation
(39 days)
The Qingdao Shuang condom is used for contraception and for prophylactic purposes (to help prevent pregnancy and the transmission of sexually transmitted diseases).
This condom is made of a natural rubber latex, which completely covers the erect penis with a closely fitted membrane. This condom is straight-walled with a reservoir tip, and is designed to conform to established national and international voluntary standards including ASTM D3492, ISO 4074, and EN 600. This condom is lubricated with silicone.
The provided document describes a 510(k) Notification for a Male Latex Condom and discusses its substantial equivalence to a predicate device. However, it does not contain detailed information about specific acceptance criteria or the study data proving the device meets those criteria.
The 510(k) summary mentions that the device is "designed to conform to established national and international voluntary standards including ASTM D3492, ISO 4074, and EN 600." It also states "This condom has the same technological characteristics as the predicate condom identified above. The design of both products conforms with ASTM Latex Condom Standard D3492 and the condom is made of natural rubber latex."
Based on this, the acceptance criteria would be those defined within these standards, and the study proving compliance would involve testing the condom's physical properties against the requirements of these standards. However, the specific results of such tests, sample sizes, ground truth establishment, or expert involvement are not detailed in this document.
Therefore, I cannot populate all the requested sections with information directly from the provided text. I will indicate where information is not available.
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Physical Properties (e.g., burst pressure, elongation, freedom from holes, dimensions) (as per ASTM D3492, ISO 4074, EN 600) | The document states the device "conforms to established national and international voluntary standards including ASTM D3492, ISO 4074, and EN 600." No specific numerical performance values are provided. |
2. Sample size used for the test set and the data provenance
- Sample size: Not specified in the provided document.
- Data provenance: Not specified in the provided document. Testing against standards would typically involve prospective testing of manufactured batches.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- This type of information (number and qualifications of experts) is not typically relevant for the performance testing of a physical product like a condom against established engineering standards. The ground truth would be based on the objective measurements and specifications defined in the standards themselves.
4. Adjudication method for the test set
- Not applicable as the assessment is against objective physical standards, not subjective interpretations requiring adjudication.
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 is not a diagnostic device involving human reader interpretation or AI.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable. This is not an AI algorithm.
7. The type of ground truth used
- The ground truth for device performance would be the objective specifications and limits outlined in the referenced voluntary standards (ASTM D3492, ISO 4074, EN 600) for male latex condoms. These standards define acceptable ranges for physical properties (e.g., burst volume, burst pressure, tensile strength, dimensions, freedom from holes).
8. The sample size for the training set
- Not applicable. This is not a machine learning model; thus, there is no "training set."
9. How the ground truth for the training set was established
- Not applicable. This is not a machine learning model; thus, there is no "training set."
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