Search Results

The STATIM 7000 Cassette Autoclave is a pressure pulse steam autoclave designed to process medical and dental instruments to achieve successful sterilization in a clinical setting. It utilizes saturated steam at high pressures in order to attain an effective kill of infectious bio-organisms and prevent cross infection.

The unit is intended to sterilize heat and moisture stable medical and dental instruments (including dental handpieces) that are commonly found in medical and dental offices, hospitals, clinics, and other facilities. The instruments must be suitable for steam sterilization at 134℃ (273%) or 121 °C (250°F). The STATIM 7000 Cassette Autoclave is not intended nor recommended for the sterilization of liquids, cloths, text les, biomedical waste and certain rubber and plastic materials specified in the Operator's Manual.

The sterilization cycles as to established times, temperatures and indicated uses for the STATIM 7000 Cassette Autoclave are as follows:

The STAT/M 7000 Cassette Autoclave is a pressure pulse steam autoclave designed to process medical and dental instruments to achieve successful sterilization. The unit utilizes saturated steam at high pressures in order to attain an effective kill of infectious bio-organisms and prevent cross infection. Instruments are placed within the provided cassette, the cassette is placed within the armature, the cycle parameters are selected and the start button depressed. The unit is then fully automatic for the complete sterilizing cycle.

The provided document, K072466, is a 510(k) Summary for the STATIM 7000 Cassette Autoclave. This document outlines the device's intended use and general characteristics, but it is not a study report proving the device meets specific acceptance criteria based on performance studies.

A 510(k) submission generally relies on demonstrating substantial equivalence to a predicate device, rather than providing extensive performance studies with detailed acceptance criteria and statistical analysis as might be found for a novel AI/software device. For a sterilizer, performance is typically confirmed through validation studies (e.g., biological indicator tests, temperature distribution studies) conducted internally by the manufacturer against recognized standards. These summaries rarely include the detailed data asked for in your prompt.

Therefore, many of the requested fields cannot be filled from the provided text because the document is not a performance study report in the way your prompt describes for a device involving AI or software analysis of data.

Here's an attempt to answer based on the available information, noting where data is not present:

1. Table of Acceptance Criteria and Reported Device Performance

The document defines the intended use and operational parameters (temperature, time) for different sterilization cycles. For a sterilizer, the ultimate acceptance criterion is achieving sterilization (i.e., effective kill of infectious bio-organisms). The "reported device performance" in this context refers to the specified cycle parameters that are intended to achieve sterilization. The document does not provide quantitative performance metrics (e.g., sterilization assurance level, spore log reduction) and a direct comparison to
acceptance criteria for those metrics.

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

• Not provided. This document is a 510(k) summary, not a detailed study report. Sterilizer performance is typically established through validation studies. The specific sample sizes (e.g., number of cycles, number of biological indicators) for such validation would not typically be in a 510(k) summary. Data provenance would likely be from the manufacturer's internal testing facilities (SciCan Ltd. in Canada).

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

• Not applicable/Not provided. For a sterilizer, "ground truth" for basic performance validation is established by objective measurements (temperature probes, pressure sensors) and the results of biological indicators (e.g., sterility achieved or not). It does not involve expert interpretation in the way an AI diagnostic device would.

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

• Not applicable/Not provided. Adjudication methods are relevant for subjective interpretations by multiple experts, which is not the case for sterilizer validation.

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 device is a sterilizer, not an AI or imaging diagnostic tool that would involve "human readers" or AI assistance in interpretation.

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

• Not applicable. This device is a mechanical/electrical sterilizer, not an algorithm.

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

• For sterilizers, the ground truth for effectiveness is typically established through:
  • Biological Indicator (BI) tests: Using highly resistant bacterial spores (e.g., Geobacillus stearothermophilus) to ensure sterilization. Failure to recover viable spores after a cycle indicates sterilization.
  • Physical Monitoring: Calibrated sensors to verify temperature, pressure, and time parameters are met throughout the sterilization chamber.
  • Chemical Indicators (CI): Strips or labels that change color when exposed to specific sterilization conditions.
• The document itself does not explicitly state which of these methods were used for validation, but these are standard for sterilizer testing.

8. The sample size for the training set

• Not applicable/Not provided. This device is a sterilizer. It does not use "training data" in the sense of machine learning. Its design and performance are based on engineering principles (thermodynamics, microbiology) and validation studies, not on learning from a dataset.

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

• Not applicable. See point 8.

Ask a specific question about this device