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The Alicia 100 Menstrual Cup / Models 99ALXS, 100ALSM, 101ALMD, 102ALLG is a receptacle placed in the vagina to collect blood and cellular debris that is extruded from the uterus via the cervix during menstruation.

The Alicia™ menstrual cup is a soft, small internally worn reusable silicone menstrual cup that holds (instead of absorbing) monthly menstrual flow. It may remain in the body for up to 12 hours. It holds an ounce of fluid. It is available four sizes:
· L size: 102ALLG; for larger built women that have had children.
· M size: 101ALMD; for women who have had vaginal childbirth or C-Section; and for women who are over 30 years old and never had childbirth.
S size: 100ALSM; for women under 30 years old who have never had childbirth or C-Section.
· XS size: 99ALXS; for teenagers and very small framed women.
The cup remains entirely within the vagina and does not tough the cervix but the stem remains outside the body to ensure retrieval of the cup.
The Alicia™ menstrual cup is manufactured from a soft silicone elastomer.

This 510(k) summary (K070965) for the Alicia™ Menstrual Cup does not contain the kind of detailed performance study data typically found in submissions for AI/ML-based medical devices. The device described is a physical menstrual cup, and the submission focuses on demonstrating substantial equivalence to a predicate device based on manufacturing materials, intended use, and general safety standards (biocompatibility).

Therefore, I cannot provide information on acceptance criteria, device performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance. These concepts are not applicable to the type of device and submission provided.

The document states:
"Verification and validation tests contained in this submission demonstrate that the difference in the submitted demonstrate that the difference in the submitted models could maintain the same safety and effectiveness as that of cleared device." This implies that such tests were performed, but the details are not included in the provided 510(k) summary. These details would typically be found in the full 510(k) submission, not the public summary.

Here's what can be extracted based on the provided text, heavily indicating that the prompt's requirements for AI/ML device testing are not met by this document:

1. Table of Acceptance Criteria and Reported Device Performance

Not applicable. The provided document is for a physical medical device (menstrual cup) and does not include performance metrics or acceptance criteria in the context of an AI/ML algorithm. The "performance" assessment is based on demonstrating substantial equivalence to a predicate device, primarily through material biocompatibility and intended use.

2. Sample Size Used for the Test Set and Data Provenance

Not applicable. The document does not describe a "test set" in the context of evaluating an algorithm's performance on data. The evaluation appears to be based on compliance with standards (e.g., ISO 10993) for the device's materials.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. There is no mention of establishing ground truth for data review, as this is not an AI/ML device.

4. Adjudication Method

Not applicable. No adjudication method is described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Not applicable. This device is a physical menstrual cup, not an AI/ML diagnostic or assistive tool, so an MRMC study is not relevant.

6. Standalone (Algorithm Only) Performance Study

Not applicable. There is no algorithm to test in a standalone manner.

7. Type of Ground Truth Used

Not applicable. There is no clinical "ground truth" established from data, as typically seen in AI/ML submissions. The "truth" for this device likely revolves around material safety and functional design.

8. Sample Size for the Training Set

Not applicable. There is no training set mentioned, as this is not an AI/ML device.

9. How the Ground Truth for the Training Set Was Established

Not applicable. There is no training set or ground truth in the context of AI/ML.

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