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The Femasys FemCerv is a sterile, disposable endocervical sampler indicated for single patient use in obtaining tissue samples from the endocervical canal for histological analysis. Clinical indications include: further evaluation of an abnormal Pap smear; cervical lesions extending into the endocervical canal; undiagnosed uterine bleeding.

The FemCerv Endocervical Sampler (FemCerv) is a sterile, disposable device that collects an endocervical tissue sample for histological evaluation.

Here's a breakdown of the requested information based on the provided text:

Acceptance Criteria and Device Performance for FemCerv™ Endocervical Sampler

1. Table of Acceptance Criteria and Reported Device Performance:

• Device functionality and integrity over cycle testing of opening and closing the sample collection chamber.
• Insertion capability in a model of the endocervical canal.
• Sheath opening functionality.
• Rotational functionality.
• Sheath closure.
• Sample release into a specimen container. | "A sample set of 29 devices was tested and passed all criteria." |
  | Shelf Life | Shelf life integrity up to 1 year accelerated aging:
• Same test methodology and acceptance criteria as bench testing. | "A sample set of 29 devices was tested and passed all criteria." |
  | Biocompatibility | ISO 10993 standards:
• Cytotoxicity.
• Irritation.
• Sensitization. | "Biocompatibility of components according to ISO 10993 standards: cytotoxicity, irritation, and sensitization was conducted and passed all criteria." |
  | Clinical Performance | Clinical Study:
• Insertion.
• Device opening and closing.
• Adequate sampling capability based upon a pathologist's evaluation. | "An 85.7% sample adequacy rate was based on sampling with 7 devices." (This is the reported performance for "adequate sampling capability") |

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

• Bench Testing: 29 devices
• Shelf Life Testing: 29 devices
• Clinical Study: 7 devices
• Data Provenance: The document does not explicitly state the country of origin. The clinical study involved subjects prior to scheduled hysterectomy, which implies a prospective collection of samples for the purpose of the study.

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

• The clinical study's sample adequacy was "based upon a pathologist's evaluation." The document does not specify the number of pathologists or their specific qualifications (e.g., years of experience).

4. Adjudication method for the test set:

• The document states that sample adequacy was based on "a pathologist's evaluation." It does not mention any specific adjudication method (e.g., 2+1, 3+1, none) among multiple experts, suggesting a single pathologist's assessment or an unstated internal process.

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:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This device is an endocervical sampler, not an AI-assisted diagnostic tool for image interpretation. Therefore, the concept of human readers improving with AI assistance is not applicable.

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

• No, this is a physical medical device for collecting tissue samples, not an algorithm. Therefore, the concept of standalone algorithm performance is not applicable.

7. The type of ground truth used:

• Clinical Study: The ground truth for sample adequacy was established by pathological evaluation of the collected tissue samples.

8. The sample size for the training set:

• The document does not describe a "training set" in the context of machine learning or AI. The tests mentioned are for verifying the physical properties and performance of the device itself.

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

• Not applicable, as there is no mention of a training set for an algorithm or AI model.

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