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The device is a personal lubricant for penile 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 and polyisoprene condoms. Not compatible with polyurethane condoms.

Vaginal Moisturizing Gel is a non-sterile, water based, colorless, transparent viscous gel. It is identical to the predicate devices except that it is packaged for use in tube sizes of either 5 or 50 grams (0.18oz. /1.8 oz.). Tubes are comprised of a laminate structure. The larger 50 gram size will utilize a flip-top cap while the smaller 5 gram size will utilize a screw on cap.

This device is compatible with natural rubber latex and polyisoprene condoms. This device is not compatible with polyurethane condoms. The following parameters are included as part of the device specification:

• Appearance
• Color
• Odor
• pH
• Viscosity
• Osmolality
• Antimicrobial effectiveness
• Total Aerobic Microbial Count (TAMC)
• Total Yeast and Mold Count (TYMC)
• Absence of Pathogenic Organisms (at minimum Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans)

The provided text describes a 510(k) premarket notification for a Vaginal Moisturizing Gel. This is a medical device submission, and the content focuses on establishing substantial equivalence to existing predicate devices, rather than presenting a study with specific acceptance criteria and detailed performance metrics as one might find for a diagnostic or AI-powered device.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts, adjudication method, MRMC study, standalone performance, training set size, how ground truth for training was established) are not applicable to this type of submission or the information provided.

However, I can extract the acceptance criteria and reported device performance from the "Physical/Chemical" and "Micro" sections, as these represent the specifications the device must meet.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "reported device performance" in the sense of a measured outcome from a study for each parameter. Instead, it lists the "Acceptance range" or "Acceptance criteria" for various characteristics, which the device is asserted to meet. The accompanying statements about "Conclusion" and "Performance Testing" imply that the device does meet these criteria.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. The submission focuses on device characteristics and substantial equivalence, not a clinical trial or performance study with a distinct "test set" as typically understood for diagnostic devices. Performance is demonstrated through meeting specified physical, chemical, and microbiological parameters, and shelf-life testing.

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)

This information is not applicable to this type of device and submission. "Ground truth" established by experts is typically relevant for diagnostic devices (e.g., image interpretation), not for a personal lubricant where physicochemical properties and microbiological purity are key.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not applicable to this type of device and submission. Adjudication methods are used in studies involving expert review of cases, which is not described here.

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 information is not applicable. This is a personal lubricant, not an AI-powered diagnostic device, so MRMC studies involving human readers and AI assistance are irrelevant.

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

This information is not applicable. This is a personal lubricant, not an algorithm, so standalone performance in that context is irrelevant.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

For the physical/chemical and microbiological characteristics, the "ground truth" is established by standard laboratory testing methods to determine if the device meets its predefined specifications. For condom compatibility, "ground truth" would be established by testing the device's interaction with condom materials as per relevant standards (implied by the claim of compatibility). For shelf-life, "ground truth" is established through accelerated aging and real-time stability studies where the device's characteristics are measured over time. There is no mention of expert consensus, pathology, or outcomes data being used as ground truth for these parameters.

8. The sample size for the training set

This information is not applicable. This is a personal lubricant; it does not involve a "training set" in the context of machine learning or AI development. The device's formulation and manufacturing process are developed, not "trained" on data.

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

This information is not applicable as there is no "training set" for this device.

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