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The Racal N95 Respirator and Surgical Mask will be used for the health care practitioner from blood and body fluids and particulates and to meet the NIOSH / CDC TB Prevention Guidelines.

RACAL N95 RESPIRATOR AND SURGICAL MASK is indicated for respiratory proceetive device. This disposable Surgical mask is a one-piece, light weight half mask with filter material forming the mask. The component includes an inner scrim layer, netting material forming the mask. The component includes an inner scrim layer, netting layer, meltblown protection layer and elastic band. RACAL N95 RESPIRATOR AND SURGICAL MASK is NIOSH approved (Approval #IC-84A-0008) and has a filtration efficiency level of 95% and is effective against particulate aerosols free of oil. RACAL NYS RESPIRATOR AND SURGICAL MASK is available in four sizes, extra small, medium and large, The nose clip and elastic headband ensures effective face to mask seal.

Here's an analysis of the provided text regarding the acceptance criteria and study for the RACAL N95 RESPIRATOR AND SURGICAL MASK:

This submission details a medical device (N95 respirator and surgical mask) rather than an AI-powered diagnostic tool. Therefore, many of the typical questions related to AI device performance evaluation (such as expert adjudication, MRMC studies, effect size of AI assistance, training set details, etc.) are not applicable to this document. The evaluation of this device is based on physical performance criteria and equivalence to predicate devices, not on data analysis or diagnostic accuracy.

Here's the information extracted and adapted to the context of this medical device:

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

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not specify a sample size for performance testing or details about the provenance of such test data. The performance claims (e.g., 95% filtration efficiency) are stated as facts met by the design and approval process rather than results from a specific study detailed in this summary. These would typically be part of the NIOSH approval process based on standardized testing protocols.

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

Not applicable. This device is evaluated against physical performance standards (e.g., filtration efficiency) and regulatory compliance (NIOSH N95 approval), not against expert clinical diagnoses.

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

Not applicable. There is no "ground truth" established by expert adjudication for this type of device. Performance is determined by standardized laboratory tests.

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-powered diagnostic device, and therefore, human reader improvement with AI assistance is not a relevant metric.

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

Not applicable. This is a physical medical device, not an algorithm.

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

The "ground truth" for this device is established by:

• Standardized Performance Specifications: Primarily derived from the Code of Federal Regulations, Title 42, Part 84 for N95-class of Respiratory Protective Devices, which outlines specific testing methodologies and passing criteria for filtration efficiency, breathability, and fit.
• Regulatory Body Approval: NIOSH (National Institute for Occupational Safety and Health) approval (TC-84A-0008) serves as the ultimate validation that the device meets the required performance standards.
• CDC Guidelines: Compliance with CDC guidelines for preventing Mycobacterium tuberculosis transmission.

8. The sample size for the training set

Not applicable. This is a manufactured physical good, not an AI model that requires a training set.

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

Not applicable. No training set is involved for this device.

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The Delta N95 Respirator and Surgical Mask will be used for the health care practitioner from blood and body fluids and particulates and to meet the NIOSH / CIX' TB Prevention Guidelines.

DELTA N95 RESPIRATOR AND SURGICAL MASK is indicated for respiratory protective device. This disposable Surgicul mask is a one-piece, light weight half mask with filter material forming the mask. The component includes an inner serim hayer, netting material forming the mask. The component includes an inner scrim layer, netting layer, meltblown protection layer, outer scrim layer and clastic band. DELTA N95 RESPIRATOR AND SURGICAL MASK is NIOSH approved (Approval #TC-84A-0003) and has a filtration efficiency level of 95% and is effective against particulate acrosols free of oil. DELTA N95 RESPIRATOR AND SURGICAL MASK is available in four sizes, extra small, small, medium and large. The nose clip and elustic headband ensures effective face to mask seal.

The provided text describes a medical device (DELTA N95 RESPIRATOR AND SURGICAL MASK) and compares it to predicate devices. However, it does not contain information about acceptance criteria, a specific study proving the device meets these criteria, or any details related to AI/algorithm performance (standalone or with human readers).

The document is a 510(k) summary for a premarket submission of a physical medical device (an N95 respirator and surgical mask), focusing on demonstrating substantial equivalence to existing devices. Therefore, a significant portion of the requested information cannot be extracted from the provided text.

Based on the information available, here's what can be provided:

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

The acceptance criteria for this device are those defined by the Code of Federal Regulations, Title 42, Part 84 for N95-class of Respiratory Protective Devices, and meeting CDC guidelines for preventing transmission of Mycobacterium tuberculosis.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

This information is not provided in the document. The submission focuses on product specifications and comparison to predicates, not specific test data results for a particular sample size.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. This type of detail is not typically included in a 510(k) summary for a physical respiratory protection device unless specific clinical studies requiring expert evaluation were conducted and reported.

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

This information is not provided in the document.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done as this is a physical medical device (respirator/mask), not an AI/software device designed to assist human readers. Thus, there is no discussion of human readers or AI assistance.

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

No, a standalone algorithm performance study was not done as this is a physical medical device (respirator/mask), not an AI/software device.

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

For this type of device, the "ground truth" would be established by standardized laboratory testing methods to measure filtration efficiency, fit, and other performance characteristics as defined by NIOSH (42 CFR Part 84) and potentially fluid resistance (as it is also a surgical mask). It's not based on expert consensus, pathology, or outcomes data in the typical sense for diagnostic/screening devices.

8. The sample size for the training set

This information is not applicable and not provided. This device is a physical product, not a machine learning model that requires a "training set."

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

This information is not applicable and not provided. As explained above, it's not a machine learning model.

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