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The provided text is a 510(k) premarket notification for a medical device (Sterile Dilator) and focuses on establishing substantial equivalence to predicate devices based on non-clinical performance testing. It does not contain information about AI/ML algorithm performance, human reader studies, or image-based diagnostic applications. Therefore, I cannot provide details on:

Acceptance criteria related to algorithm performance (e.g., sensitivity, specificity, AUC).
Sample sizes for algorithm test sets (image data).
Data provenance for AI/ML models.
Number of experts for ground truth establishment in an AI context.
Adjudication methods for AI ground truth.
MRMC comparative effectiveness studies.
Standalone algorithm performance.
Type of ground truth (e.g., pathology, outcomes data).
Training set sample size or how ground truth was established for training data.

The document discusses acceptance criteria and studies related to the physical and mechanical performance of the Sterile Dilator itself, ensuring it is equivalent to the predicate devices for its intended use (percutaneous introduction of guidewires).

Here's the information that can be extracted regarding the physical device's performance testing:

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

The document presents performance testing conducted on the Galt sterile dilators.

Attributed Tested	Specification	Reported Device Performance (Results)
Dimensional	Comparison between predicate device and subject device, including specific ranges for ID at Tip, Effective length, and Through hole ID for various dilator sizes (e.g., Min size 3F, Most common long length 9F, Max size 16F).	Passed: All sampled dilator sizes within spec range
Descriptive	Comparison to predicate including labeling (labels, IFU, promotional materials), intended use and instructions, and materials used to fabricate the devices.	Passed: All labeling, IFU and promotional materials were identical to predicate.
Mechanical specifications	Predicate Spec range: Tensile strength of dilator body - min 5lbs; Tensile strength of dilator body to hub - min 5lbs.	Passed: All sampled dilator sizes passed test within acceptance range.
Particulate Test	Confirm acceptable levels of particulate.	Passed: Test article exhibited particulate levels equal to or less than predicate equal.
Simulated use Test	Measure Insertion force; Force must be ≤ 20% of predicate.	Passed: All sampled dilators within range.
Packaging Test	Packaged should not be damaged after all tests. Seal must remain intact. Shipping test Pouch Test worst case scenario Atmospheric conditioning ISTA 2A-2011 Partial simulation performance test procedure for packaged products 150lb (68kg) or less.	Passed: No package was damaged. Visual inspection passed. No pouches were damaged.

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

The document does not specify exact sample sizes (N numbers) for each physical/mechanical test. It generally states a qualitative "All sampled dilator sizes" or "Test article."

Data Provenance: The tests were conducted to evaluate the Galt sterile dilators against pre-defined product specifications, which were based on existing predicate devices (K172487, K153533, K173287). The context implies these are laboratory-based, non-clinical tests. No country of origin for test data is mentioned, but the manufacturer is Galt Medical Corporation, Garland, TX, USA. The study design is implied to be prospective testing for device validation rather than retrospective data analysis.

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

This concept is not applicable to the non-clinical, physical/mechanical testing described. Ground truth for these tests is established by engineering specifications, material properties, and standardized testing protocols (e.g., tensile strength, dimensional measurements, particulate levels). No human expert interpretation of results (like in an AI imaging study) is mentioned or implied beyond standard laboratory personnel conducting and verifying tests.

4. Adjudication method for the test set

Not applicable. The tests are objective quantitative measurements or visual inspections against predetermined engineering specifications, not subjective assessments requiring expert consensus or adjudication.

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 not an AI/ML device or a diagnostic device involving human readers or image interpretation.

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

Not applicable. This is not an AI/ML algorithm.

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

The "ground truth" for the device's performance is based on established engineering specifications, mechanical test standards, and equivalence to the performance of predicate devices as measured through physical and simulated use tests. For example:

Dimensional: Measured dimensions compared against specified ranges derived from predicate devices.
Mechanical: Measured tensile strength compared against minimum force requirements.
Particulate: Measured particulate levels compared against acceptable limits derived from predicate device performance.
Simulated Use: Measured insertion force compared against a specified percentage (≤ 20%) of the predicate's force.
Packaging: Visual inspection and performance against established packaging integrity standards (ISTA 2A-2011).

8. The sample size for the training set

Not applicable. This is not an AI/ML model that requires a training set.

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

Not applicable. There is no training set for an AI/ML model.

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