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The Wet Water Based Personal Lubricant (additionally, branded as WET Platinum Houston, Elite Water-Based Hybrid) is a personal lubricant for penile, anal and/or vaginal application, intended to moisturize and lubricate, to enhance the ease and comfort of intimate sexual activity and supplement the body's natural lubrication. This product is compatible with natural rubber latex and polyisoprene condoms. This product is not compatible with polyurethane condoms.

The Wet Water Based Personal Lubricant is a non-sterile, water and silicone blend personal lubricant that provides lubrication during intimate sexual activity. This device is compatible with natural rubber latex and polyisoprene condoms, and is not compatible with polyurethane condoms. The Wet Water Based Personal Lubricant is provided in 3 oz.high density polyethylene terephthalate bottle is fitted with dispensing bottle cap with a disc-top. Wet Water Based Personal Lubricant is a personal lubricant for over-the-counter (OTC) use.

The provided text describes the acceptance criteria and performance of the "Wet® Water Based Personal Lubricant" device, including its compatibility with condoms and biocompatibility.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

• Sample Size: Not explicitly stated for each specific test. The document mentions "results of testing" and "results of accelerated and real-time aging studies" without providing specific numbers of samples tested.
• Data Provenance: The data is generated from performance testing conducted by the manufacturer, Trigg Laboratories, Inc., as part of their 510(k) submission to the FDA. The provenance of external data used for comparison (e.g., predicate device data) is not detailed beyond referencing the predicate K number. The studies appear to be prospective for the subject device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not provided in the document. The tests performed are laboratory-based and generally do not involve human "experts" establishing a ground truth in the way a clinical study would. The acceptance criteria are based on established scientific and regulatory standards (e.g., USP, ISO, ASTM).

4. Adjudication method for the test set:

This information is not applicable as the tests are for physical, chemical, and microbiological properties, as well as biocompatibility, which are objectively measured against predefined standards rather than adjudicated by experts.

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:

This is not applicable. The device is a personal lubricant, not an AI-powered diagnostic or imaging device, and therefore does not involve human readers or AI assistance in its use or evaluation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

This is not applicable. The device is a physical product (lubricant), not an algorithm or software.

7. The type of ground truth used:

The "ground truth" for the device's performance is based on:

• Pre-defined chemical and physical specifications: For properties like appearance, color, odor, viscosity, specific gravity, pH, and osmolality.
• Established microbiological standards: USP , , , and for antimicrobial effectiveness and microbial counts.
• International Biocompatibility Standards: ISO 10993-1, 10993-5, 10993-10, 10993-11.
• Condom Compatibility Standards: ASTM D7661-10.
• Accelerated and Real-time Aging Studies: To determine shelf-life.

These are objective standards and laboratory test results, not expert consensus, pathology, or outcomes data in the clinical sense.

8. The sample size for the training set:

This is not applicable. As a physical product, there is no "training set" in the context of machine learning or AI models. The device formulation and manufacturing process would be developed and refined through R&D, but this is not analogous to a training set for an algorithm.

9. How the ground truth for the training set was established:

This is not applicable for the same reasons as point 8.

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