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The device is intended for medical purposes to provide mobility to persons restricted to a sitting position.

The proposed device, Wheelchair model HM303, is traditional manually operated, user propelled, manual, mechanical wheelchairs. The device can be foldable easily for transport.

The main frame is made of magnesium alloy frame, which has a seat base with four-wheeled with a back cover. Upon the outside of this framework, and to the rear, are assembled two axle plates, wheels are connected to the stainless steel axle receivers via stainless steel axles. On the front end of the frame are assembled two caster housings. Caster forks are mounted to these housings via steel axles. On the front end of the frame, upon the caster, two footplates are assembled for foot holding. Two armrests are mounted on the frame for user's arm holding. Two grips mounted on the top end of the frame with a brake, and another brake is designed under the seat base. A folden set is on the frame, under the grips, for folding backrest.

The provided text does not contain information about acceptance criteria and a study that proves a device meets those criteria in the context of a medical device with an AI component or a comparative effectiveness study.

The document is a 510(k) premarket notification for a Wheelchair, which is a mechanical device, not an AI-powered one. The submission focuses on demonstrating substantial equivalence to a predicate mechanical wheelchair through non-clinical performance and biocompatibility testing against established ISO standards.

Here's why the requested information cannot be extracted from the provided text:

• No AI Component: The device described is a "Mechanical Wheelchair" (Model HM303). There is no mention of any artificial intelligence, machine learning, or software algorithm components.
• No Clinical Study for Effectiveness: The document explicitly states "No Clinical Test conducted" in Section 8. The evaluation relies solely on non-clinical tests (physical performance and biocompatibility) and comparison to a predicate device.
• No Comparative Effectiveness Study: Since there's no AI component and no clinical study, there is no multi-reader multi-case (MRMC) comparative effectiveness study or analysis of human reader improvement with AI assistance.
• No Standalone Algorithm Performance: The concept of "standalone (algorithm only without human-in-the-loop performance)" is inapplicable because there is no algorithm or AI involved.
• No Ground Truth for AI Models: The establishment of ground truth for training or test sets is relevant for AI/ML models. For a mechanical wheelchair, performance is assessed against physical standards, not against expert labels or pathological outcomes for diagnostic accuracy.

Therefore, I cannot provide the requested information about acceptance criteria and a study proving the device meets them in the context of AI/ML or comparative effectiveness from the given document.

The document details the following regarding performance and acceptance for a mechanical wheelchair:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance: Not applicable. For mechanical device testing against ISO standards, the "sample size" refers to the number of units tested, which is not specified but is typically a small number (e.g., 1-3 units) unless stress testing multiple units. The tests are non-clinical, likely performed in China (country of origin for the manufacturer).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for mechanical performance and biocompatibility is established by the specifications of international standards (ISO) and laboratory test results, not by expert interpretation in the medical sense.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. Adjudication methods are relevant for subjective interpretations (e.g., image reading). Mechanical and biocompatibility tests have objective pass/fail criteria based on 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, as this is not an AI device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable, as this is not an AI device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The "ground truth" for this mechanical device is defined by international standards (ISO) for safety, performance, and biocompatibility, as well as FDA regulatory requirements for labeling.

8. The sample size for the training set: Not applicable. This is a mechanical device, not an AI/ML model that requires a training set.

9. How the ground truth for the training set was established: Not applicable.

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