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The Family of steam Sterilizers B 28 series B CLASSIC-28 and B FUTURA-28 models are table top steam sterilizers to be used in medical and dental practices, hospitals, clinics, nursing homes, and other facilities to sterilize reusable surgical instruments (including dental handpieces) and medical materials, heat and moisture resistant and compatible with steam sterilization process.

The Family of Steam Sterilizers B 28 series is validation of fabric packs/textiles for sterilization programs C1 and C2.

The Family of Steam Sterilizers B 28 series is validated for sterilization up to 9 dental handpieces for sterilization programs C1 and C2.

The Family of Steam Sterilizers B 28 series is validated for sterilization up to 6 lumen devices (specifications in the table below)

The device shall not be used for the sterilization of fluids, liquids or pharmaceutical products.

The Family of steam sterilizers B 28 series includes table-top steam sterilizers that use saturated steam at high pressures and temperatures to kill infectious bio-organisms. The B Classic-28 and B Futura-28 models differ only for some minor characteristics such as color touchscreen, optional Wi-FI connection, illumination of the working area and Ethernet connection

The steam is generated by a steam generator connected to the sterilization chamber without disconnecting devices. An electric heating element surrounds the chamber for preheating of the chamber and maintaining the temperature. The subject family is characterized by a fractionated vacuum system for the complete air removal also from hollow and porous materials as well as by a final vacuum drying phase able to eliminate any trace of condensation from the load.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

This document is a 510(k) summary for a medical device (CEFLA S.C.'s Family of Steam Sterilizers B28 series), which focuses on demonstrating substantial equivalence to predicate devices rather than proving novel clinical effectiveness with detailed studies involving human readers or extensive ground truth adjudication. Therefore, many of the requested points from the prompt (like "multi-reader multi-case (MRMC) comparative effectiveness study," "effect size of how much human readers improve with AI vs without AI assistance," "number of experts used to establish the ground truth," etc.) are not applicable to this type of regulatory submission and information.

The acceptance criteria here are based on the performance standards for steam sterilizers, particularly AAMI ST55:2010. The "study" refers to the validation tests performed to ensure compliance with these standards.

1. A table of acceptance criteria and the reported device performance:

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

• Sample Size for Test Set: The document mentions "each cycle tested" for biological indicators and moisture retention tests, and "worst case loads" for temperature verification and moisture retention. However, it does not specify the exact number of cycles or loads used for these tests. It indicates the use of "a FDA cleared Bowie-Dick test pack" for that specific test, implying a single test pack per run.
• Data Provenance: The tests were performed to validate the sterilizer's design and sterilization process according to AAMI ST55:2010 requirements. The manufacturer is CEFLA S.C. from Italy. The context suggests these were prospective validation tests conducted by the manufacturer or a certified testing facility as part of the regulatory submission process.

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)

• Not applicable. The ground truth for sterilizer performance is established by objective scientific/engineering standards like Sterility Assurance Level (SAL), temperature readings, leak rates, and biological indicator growth/no growth, not by expert interpretation of complex data.

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

• Not applicable. Adjudication methods like 2+1 or 3+1 are typically used for subjective assessments where multiple human readers interpret medical images or clinical data. Sterilizer validation relies on objective measurements against predefined criteria.

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 a submission for a steam sterilizer, which is a physical device, not an AI-powered diagnostic tool used by human readers.

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

• Not applicable in the context of an "algorithm only" performance. The device itself (the sterilizer) performs its function autonomously, but its "performance" is verified through engineering and biological tests, not by an algorithm's output.

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

• The ground truth is derived from objective predefined performance standards and direct measurements:
  • Sterility Assurance Level (SAL) of 10⁻⁶ reduction: Verified by biological indicators (which either show growth or no growth after exposure).
  • Temperature and pressure readings: Measured by calibrated sensors during cycles.
  • Bowie-Dick test results: Visual confirmation of uniform color change on a test indicator.
  • Air leak rate: Measured quantitatively.
  • Moisture retention: Measured quantitatively as percentage weight increase.
  • Compliance with recognized electrical safety (IEC 61010 series), EMC (IEC 61326-1), and software quality (IEC 62304) standards.

8. The sample size for the training set

• Not applicable. This is a physical sterilizer, not a machine learning model that requires a training set. The "training" for such a device would be its mechanical and electrical design and manufacturing process, which are subject to engineering and quality control, not data training.

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

• Not applicable. As there is no training set in the AI/ML sense, there is no ground truth established for it. The integrity and functionality of the device are ensured through its design specifications, manufacturing processes, and adherence to relevant industry standards.

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