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The Praxiject™ 0.9% NaCl prefilled syringe with 0.9% Sodium Chloride Injection, USP, is intended only for flushing vascular access devices. May be placed on a sterile field.

The Praxiject™ 0.9% NaCl prefilled syringe is a single use plastic piston syringe with a Luer lock connection fitting, prefilled to labeled fill volume with 0.9% Sodium Chloride Injection, USP, with no preservatives (normal saline), and capped with a plastic tip cap. Each prefilled syringe is individually packaged in a plastic peel pouch and terminally sterilized by gamma irradiation.

This document is a 510(k) Pre-market Notification for a medical device called Praxiject™ 0.9% NaCl, which is a prefilled syringe. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets specific performance criteria through clinical studies or AI algorithm validation studies.

Therefore, many of the requested details about acceptance criteria, study types, sample sizes, and expert ground truth are not applicable to this document as it pertains to a traditional medical device (a prefilled syringe) and not an AI/ML-driven diagnostic or treatment device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present "acceptance criteria" in the context of an AI algorithm's performance metrics (e.g., sensitivity, specificity, AUC). Instead, it presents performance standards and results for the physical and chemical characteristics of the prefilled syringe.

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

• Not applicable. This document describes testing for a physical medical device (syringe), not an AI algorithm. The "test set" would refer to samples of the manufactured syringes and their components, tested in a laboratory setting according to the listed standards. There is no mention of "country of origin of the data" or "retrospective/prospective" as these pertain to clinical data for AI/ML validation.

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

• Not applicable. Ground truth in the context of this document refers to established standards and laboratory measurements for chemical and physical properties, not expert clinical interpretation. The "experts" would be laboratory technicians and chemists performing the tests according to standardized protocols.

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

• Not applicable. This concept pertains to resolving discrepancies among expert readers in AI/ML validation studies. For direct physical and chemical testing, the results are typically objectively measured against pre-defined specifications in the performance standards.

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 traditional medical device (syringe), not an AI/ML system, so such a study would not be performed.

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

• Not applicable. This is a physical device, not an algorithm.

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

• The "ground truth" for this device's performance is established by conformance to recognized consensus standards (e.g., USP, ISO standards) and laboratory testing methods that define acceptable ranges and limits for physical, chemical, and biological properties. This is an objective, standardized ground truth based on scientific principles and established testing methodologies.

8. The sample size for the training set

• Not applicable. This is a physical medical device. There is no "training set" in the context of this 510(k) submission.

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

• Not applicable. As there is no training set for an AI algorithm, this question does not apply.

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