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The proposed device is intended for the subcutaneous injection of U-100 Insulin.
Kendall Monoject® Insulin Syringes are intended for subcutaneous injection of U-100 insulin.

These devices are sterile, single use, disposable hypodermic syringes with permanently affixed hypodermic needles. Monoject Insulin Syringes consist of a syringe barrel, a plunger rod, and a hypodermic needle permanently affixed to the tip of the syringe with epoxy. Monoject Insulin Syringes are available in 1.0 cc (100 units), 0.5 cc (50 Units) and 0.3 cc (30 units) syringe capacities with a 30g x 5/16 inch needle.

The provided text describes the 510(k) summary for the Monoject® Insulin Syringe, which focuses on a design change involving a new, smaller gauge needle. The submission aims to demonstrate substantial equivalence to existing legally marketed devices.

However, the document does not contain the detailed information required to fill out the table and answer the study-related questions thoroughly. Specifically, it lacks:

• Explicit acceptance criteria with specific numerical targets. The document mentions conformance to ISO 8537:1991(E) but doesn't detail which specific parameters were tested against which criteria.
• Reported device performance in a quantifiable manner against predefined criteria.
• Descriptions of a study methodology that would include sample sizes for test sets, data provenance, expert involvement for ground truth, adjudication methods, MRMC studies, or standalone algorithm performance.
• Details on the training set for an AI/algorithm, as this is a physical medical device (syringe with a needle), not an AI/software device.
• The type and establishment of ground truth in the context of an AI/algorithm, as it's not applicable here.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can be extracted or inferred based on the document's nature (510(k) for a physical device) and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified in the provided text. For a physical device like a syringe, testing would involve batches of manufactured units.
• Data Provenance: Not specified, but generally, for such devices, testing is conducted within the manufacturer's facilities or certified testing laboratories in the country of manufacture (e.g., USA, as the company is based in Mansfield, MA). The testing would be prospective, occurring during design verification and validation.

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

• Not Applicable. This question is typically relevant for AI/software devices where "ground truth" relates to clinical annotations or diagnoses. For a physical device, testing involves engineering and laboratory assessments against functional and safety standards (e.g., material scientists, quality engineers, metrology specialists, sterility experts). No clinical expert "ground truth" establishment as understood in AI studies is mentioned or implied for this type of device submission.

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

• Not Applicable. This is a concept used in expert-based annotation or labeling for AI model training/testing. For a physical device, compliance is determined by objective measurement against engineering specifications and regulatory standards, typically through pass/fail 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

• No. An MRMC study is not relevant here as the device is a physical insulin syringe, not an AI/software product or an imaging device requiring human interpretation.

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

• No. This question applies to AI/software. This is a physical medical device.

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

• For a physical device like an insulin syringe, "ground truth" refers to established engineering specifications, medical device standards (like ISO 8537), and regulatory requirements for safety and performance (e.g., sterility, biocompatibility, dosage accuracy, material properties, mechanical strength, needle sharpness). It's based on objective measurements and laboratory tests rather than clinical "expert consensus" on an image or diagnosis.

8. The sample size for the training set

• Not applicable. This question pertains to AI/machine learning models. A physical device does not have a "training set" in this context. Design and manufacturing processes are refined through engineering, quality control, and testing, but not "trained" with data in the same way an algorithm is.

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

• Not applicable. As above, there is no "training set" for a physical device.

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