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The Compact Auto Safety Injector (CASI) is indicated for assisting the selfadministered subcutaneous injection of fixed doses of FDA approved drug products with viscous liquid formulations, which are presented in standard 1ml long BD Hypak® pre-filled syringes with staked needles. The CASI is primarily intended for home use by patients to aid and support their treatment regime or may be used by Health Care Professionals or caregivers.

The CASI is a single-use, automatic, disposable and hidden-needle auto-injector for the self-administration of liquid drug products.

The provided document is a 510(k) summary for the Compact Auto-Safety Injector (CASI) and a subsequent FDA clearance letter. It outlines the device's description, intended use, and comparison to a predicate device, as well as a general statement about performance testing.

However, the document does not contain specific details regarding acceptance criteria, reported device performance tables, sample sizes for test or training sets, data provenance, number or qualifications of experts for ground truth, adjudication methods, or specific results of MRMC or standalone studies.

Instead, the document primarily focuses on establishing substantial equivalence to a predicate device, a common pathway for medical device clearance in the US.

Here's a breakdown of the information that can be extracted and what is missing:

1. Acceptance Criteria and Reported Device Performance

Missing. The document states that "Extensive design verification, functional and performance testing have been conducted" and that "The information provided in this premarket notification demonstrates that the CASI device is safe and effective for the intended use and is substantially equivalent to the legally marketed predicate device." However, it does not provide a table specifying acceptance criteria or the numerical performance metrics achieved by the CASI device against those criteria.

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

Missing. The document mentions "extensive design verification, functional and performance testing" but does not provide details on the sample size of the test sets used for these evaluations. It also does not specify data provenance (e.g., country of origin, retrospective/prospective).

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

Missing. This information is not present in the provided 510(k) summary. The testing described appears to be primarily engineering-focused (design verification, functional, and performance testing) rather than clinical studies requiring expert ground truth establishment in a diagnostic context.

4. Adjudication method for the test set

Missing. As the document doesn't detail the types of tests that would require adjudication (e.g., image interpretation), this information is not provided.

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

Missing. The CASI is an auto-injector device, not an AI-powered diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is irrelevant and not mentioned.

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

Missing. Again, this is an auto-injector, not an algorithm. The "performance tests" would refer to mechanical and functional aspects of the device itself, not an algorithm's standalone performance.

7. The type of ground truth used

Missing. Given the nature of the device (an auto-injector), "ground truth" would likely relate to objective measurements of its mechanical function (e.g., dose delivery accuracy, needle retraction success, injection force, usability). The document does not specify what objective standards or methods were used to determine the "truth" for these functional tests.

8. The sample size for the training set

Missing. As this device is not a machine learning or AI-based product, there would be no "training set" in the computational sense. The "design verification" and "performance testing" are typically conducted on prototypes or production units of the device.

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

Missing. As there is no "training set" in the AI sense, this information is not applicable and therefore not provided.

In summary, the provided 510(k) document is a regulatory submission focused on demonstrating substantial equivalence of a medical device (an auto-injector) to a predicate device. It confirms that performance tests were conducted to ensure safety and effectiveness but does not delve into the detailed methodological aspects of those tests that would be relevant for an AI/ML-based device evaluation (e.g., specific acceptance criteria tables, sample sizes, expert involvement, or AI-specific study types).

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