Search Results

The PFR95TM Particulate Filter Respirator and Surgical Masks are intended for use by operating room personnel and other health care workers to protect both the patients and the health care workers from transfer of microorganisms, blood and body fluids, and airborne particulate materials.

Respirator consisting of nonwoven inter facing, filter media(s), and an outer facing. It covers the nose and mouth of the wearer, and is held in place with two synthetic elastic headbands, conforming to the curvature of the wearer's nose with a malleable nosepiece.

The provided 510(k) summary for the Tecnol PFR95™ Particulate Filter Respirator and Surgical Mask (K974068) describes the device, its intended use, and performance data from non-clinical tests. Since this is a submission for a physical medical device (particulate filter respirator/surgical mask) and not a software-as-a-medical-device (SaMD), many of the requested criteria (such as sample size for test/training sets, experts for ground truth, adjudication methods, MRMC studies, or standalone algorithm performance) are not applicable or typically reported for this type of submission. The information provided focuses on material properties and physical performance rather than AI/algorithm performance.

Here's an analysis of the available information based on the prompt's request:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated. The document refers to "Subject device samples" for each test. For physical device testing like this, the sample size would typically be defined by the relevant standards (e.g., NIOSH, ISO).
• Data Provenance: Not specified. This would be laboratory testing conducted on the manufactured device, not data from human subjects or clinical records.

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

• Not Applicable. The "ground truth" for this type of device is established through performance against objective, standardized physical and chemical tests (e.g., NIOSH standards for filtration, ISO standards for biocompatibility). There is no human expert consensus for defining ground truth in this context.

4. Adjudication Method for the Test Set

• Not Applicable. As ground truth relies on objective laboratory measurements against defined standards, there is no need for an adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No. An MRMC study is relevant for diagnostic imaging or similar scenarios where human readers interpret medical data, often with and without AI assistance. This is a physical device, so such a study is not applicable.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• No. This is not a software algorithm; it is a physical medical device. Therefore, a "standalone algorithm performance" study is not applicable. The performance described is the standalone performance of the physical device itself.

7. The Type of Ground Truth Used

• Standardized Test Results/Compliance with Regulatory Standards. The "ground truth" is adherence to established national and international standards (e.g., NIOSH for filtration, airflow resistance, and fluid resistance; ISO-10993 for biocompatibility). These standards define objective, measurable pass/fail criteria.

8. The Sample Size for the Training Set

• Not Applicable. This is not an AI/ML device that requires a training set.

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

• Not Applicable. This is not an AI/ML device that requires a training set.

Ask a specific question about this device

Meets the CDC guidelines for TB exposure control. Has a filter efficiency level of 95% against solid particulate aerosols free of oil (Type N95 respirator). Designed to be fluid resistant to splash and spatter of blood and body fluids.

Respirator consisting of nonwoven inter facing, filter media(s), a fluid barrier film, and an outer facing. It covers the nose and mouth of the wearer, and is held in place with two synthetic clastic headbands, conforming to the curvature of the wearer's nose with a malleable nosepiece.

This document is a 510(k) Premarket Notification for a respirator and surgical mask. It primarily focuses on demonstrating equivalence to a predicate device through performance testing. Therefore, it does not involve the type of study design or criteria typically used for AI-driven medical devices, which the provided questions are geared towards.

Here's an analysis based on the information provided, highlighting why many questions are not directly applicable:

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

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

The document mentions "Subject device samples" for each test but does not specify the exact sample size for any of the performance tests. The provenance of the data (country of origin, retrospective/prospective) is not stated, but given it's a 510(k) submission, it's presumed to be data generated by the manufacturer for the specific device, likely through prospective testing.

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 question is not applicable to the provided document. The device is a physical product (respirator/surgical mask), and its performance is evaluated against established physical and material standards (e.g., NIOSH, fluid resistance). There is no "ground truth" derived from expert interpretation of data.

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

This question is not applicable. Adjudication methods like 2+1 are used in clinical studies where multiple experts interpret medical images or clinical data. Here, objective physical tests are performed.

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

This question is not applicable. MRMC studies are used for evaluating diagnostic devices, often involving AI, where human readers interpret cases. This document concerns a physical barrier device.

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

This question is not applicable. There is no algorithm or AI component in this device.

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

The "ground truth" for this device's performance is established by objective engineering and material science standards and regulatory requirements (e.g., NIOSH standards for filtration efficiency and airflow resistance, simulated fluid challenge for fluid resistance). There is no "expert consensus," "pathology," or "outcomes data" in the typical medical imaging sense.

8. The sample size for the training set

This question is not applicable. There is no AI model or algorithm that requires a training set for this device.

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

This question is not applicable for the same reason as above; there is no training set.

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device

Ask a specific question about this device