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Powder Free Blue Patient Nitrile Examination Gloves is a disposable device intended for medical purposes that is worn on the examiner's hand or finger to prevent contamination between patient and examiner.

Nitrile Powder Free Patient Examination Gloves that meets all of the requirements of ASTM standard D 6319-05

The provided document describes the acceptance criteria and the study conducted for the Nitrile Powder Free Patient Examination Gloves (Blue). This is a medical device subject to regulatory clearance, and the study focuses on non-clinical performance to demonstrate substantial equivalence to a predicate device.

Here's an analysis of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the sample sizes used for each specific test (e.g., number of gloves tested for dimension, physical properties, or pinholes). However, the tests are conducted as per ASTM standard D 6319-05, 21 CFR 800.20, ASTM standard D 5250-06, D6124-01, and ISO10993-10. These standards typically define the sampling plans and statistical methods for evaluating product quality.

Most of these tests are likely retrospective in the sense that they are performed on manufactured batches of the device. The data provenance is from China, where the manufacturer (TANGSHAN ZHONGHONG PULIN GROUP CO., LTD.) is located.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This type of device (patient examination gloves) and the nature of the tests performed do not typically involve human experts to establish ground truth in the way an AI diagnostic algorithm would. The "ground truth" for these tests is defined by objective, measurable laboratory standards (e.g., specific dimensions, tensile strength measurements, water leak test results, chemical analysis for powder, animal model responses for biocompatibility). These are evaluated by trained laboratory personnel following standardized protocols, not by clinical "experts" in the traditional sense of clinicians establishing a diagnosis.

4. Adjudication Method for the Test Set

Adjudication methods like "2+1" or "3+1" are relevant for subjective assessments, often when humans are interpreting images or clinical data. Since the tests for this device are objective and based on established industry standards and laboratory procedures, no formal adjudication method is mentioned or typically needed. The results are quantitative measurements or pass/fail criteria against pre-defined thresholds.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was done. This type of study is typically conducted for diagnostic or AI-assisted interpretation devices where human performance is a key factor, often involving multiple radiologists or clinicians evaluating cases with and without AI assistance to measure improvement in accuracy or efficiency. Patient examination gloves are a much simpler device, evaluated for basic physical properties and biocompatibility.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No standalone study was done. This concept is specifically relevant to AI/ML algorithms, where the algorithm's performance is evaluated independently of human interaction. Since the device in question is a physical product (gloves) and not a software algorithm, this type of study is not applicable.

7. Type of Ground Truth Used

The "ground truth" for this device's evaluation is primarily based on:

• Objective laboratory measurements: for characteristics like dimension, physical properties (e.g., tensile strength, elongation), and powder residual.
• Standardized test methods: for "freedom from pinholes" (water leak test).
• Animal model studies: for biocompatibility (primary skin irritation in rabbits and dermal sensitization in guinea pigs).

There is no "expert consensus," "pathology," or "outcomes data" in the clinical sense used to establish ground truth for these non-clinical performance metrics.

8. Sample Size for the Training Set

The concept of a "training set" is not applicable to this device. Training sets are used in machine learning for developing AI algorithms. This device is a physical product being evaluated against engineering and biological performance standards, not an AI model.

9. How the Ground Truth for the Training Set Was Established

As there is no "training set" for this device, the question of how its ground truth was established is not applicable.

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