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Pen Needle is intended for use with pen injector devices for the subcutaneous injection of drugs.

The proposed device, Pen Needle, is designed for use with pen injectors for subcutaneous injection of a desired dose of drugs approved for delivery using a pen needle. It is provided sterile, single use. The proposed device consists of four components, which are 1) Needle Container 2) Needle Shield 3) Needle Tube and 4) Needle Hub. Needle Container, together with Sealed Paper can forming the unit packaging of Pen Needle, that maintains the sterility of Pen Needle Hub can be connected screwed onto the pen injectors.

The provided document is a 510(k) premarket notification for a medical device (Pen Needle) and focuses on demonstrating substantial equivalence to a predicate device. It is not an AI/ML medical device submission. Therefore, the document does not contain information typically found in an AI/ML medical device acceptance criteria and study section, such as:

• A table of acceptance criteria and reported device performance for an AI/ML algorithm.
• Sample sizes used for a test set or data provenance for an AI/ML algorithm.
• Number of experts or qualifications of experts used for AI ground truth.
• Adjudication methods for AI test sets.
• Multi-Reader Multi-Case (MRMC) comparative effectiveness studies.
• Standalone AI performance.
• Type of ground truth used for AI/ML.
• Sample size or ground truth establishment for an AI/ML training set.

The document discusses non-clinical tests performed to verify the Pen Needle met design specifications and complied with ISO standards, focusing on manufacturing quality, sterility, and biocompatibility rather than an AI algorithm's performance.

Therefore, I cannot extract the requested information as it is not present in the provided text.

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The DROPLET® PEN NEEDLE 34G is intended for use with pen injector devices for the subcutaneous injection of drugs.

DROPLET® PEN NEEDLES 34G are sterile, single use needles intended for use with pen injector devices for the subcutaneous injection of drugs. The pen needles are OTC devices. The pen needle assembly consists of a double-ended into an injection molded hub using adhesive. The hub has internal threads, which allow it to be screwed onto the pen injector device. This allows the cartridge end of the cannula to penetrate through the rubber septum of the cartridge. The patient end of the cannula are lubricated using a siliconebased lubricant for ease of injection and rubber septum penetration. There is an inner needle shield assembled over the cannula to protect the needle point from damage and accidental needle sticks. There is also an outer cover. Each pen needle assembly is protected with a peel away seal to provide a sterility barrier.

The provided text is a 510(k) Summary for the DROPLET® PEN NEEDLE 34G. It details the device's characteristics and compares it to predicate devices to establish substantial equivalence, rather than describing a study proving the device meets specific acceptance criteria in the context of diagnostic performance or clinical outcomes.

Therefore, many of the requested elements for describing "acceptance criteria and the study that proves the device meets the acceptance criteria" are not applicable or cannot be extracted directly from this document. The document focuses on regulatory clearance based on substantial equivalence to existing devices, primarily through non-clinical performance and scientific rationale, rather than a clinical trial demonstrating diagnostic or treatment effectiveness against specific performance metrics.

However, I can extract the acceptance criteria related to non-clinical performance and the "study" (bench testing and validations) that demonstrates compliance with those criteria.

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

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

The document does not specify the exact sample sizes (N numbers) for each individual non-clinical test (e.g., how many needles were tested for flow rate, or how many units for package integrity). It refers to compliance with ISO standards which typically involve specific sampling plans. The data provenance is internal testing performed by HTL-STREFA S.A. (manufacturer located in Poland). The studies are non-clinical (bench testing, lab validations).

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

Not applicable. This is a non-clinical device clearance based on engineering and material performance standards. There is no "ground truth" established by medical experts for diagnostic or clinical performance. Compliance with standards like ISO 11608-2:2012 is generally assessed by qualified technicians and engineers.

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

Not applicable. This is not a clinical study involving human reader interpretation. Test results are objective measurements against defined 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 medical device (pen needle) and the submission is for non-clinical performance and substantial equivalence, not an AI-powered diagnostic or assistive technology.

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

Not applicable. This is not an algorithm or AI device.

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

For non-clinical performance, the "ground truth" is adherence to established international standards and specifications (e.g., ISO 11608-2:2012 for needle performance, ISO 10993-1 for biocompatibility, ISO 11135-7:2014 for sterilization). These standards define acceptable limits and methodologies.

8. The sample size for the training set

Not applicable. There is no "training set" as this is not a machine learning model.

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

Not applicable. There is no "training set" as this is not a machine learning model.

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