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The 3M™ Bair Hugger™ Universal Warming Gown made with Thinsulate™ Insulation is part of the 3M patient temperature management system') when used with Bair Hugger™ 700 series or 675 warming unit. The system, monitored and controlled by a trained clinician, is used perioperatively to prevent and treat hypothermia.

The 3M™ Bair Hugger™ Universal Warming Gown made with Thinsulate™ Insulation is a disposable, single-patient perioperative warming gown with 3M™ Thinsulate™ Insulation. The warming gown is intended to be worn throughout the entire journey: before, during and after surgery.

Bair Hugger™ warming gowns are part of the Bair Hugger™ warming system; there are two parts to the system: the warming gown and warming unit (temperature management unit). The Universal Warming Gown is available in three sizes (small, standard, and x-large) and is intended to be used with the Bair Hugger™ 700 series or 675 warming unit. The system, monitored and controlled by a trained clinician, is used perioperatively to prevent and treat hypothermia.

The warming gown has two patient warming options: an integrated lower body insert and a removable multi-position warming blanket. The lower body insert has a lower midline hose port, and the warming blanket is to be removed from a pocket in the front upper section of the gown. The warming blanket can be used alone or in combination with the warming gown during surgery.

After surgery, the warming gown should be re-domed and used to warm the patient using the lower midline hose port.

The provided text describes a 510(k) premarket notification for a medical device: the "3M™ Bair Hugger™ Universal Warming Gown made with Thinsulate™ Insulation." This document focuses on proving substantial equivalence to a predicate device, rather than providing a detailed clinical study demonstrating improved patient outcomes or AI performance.

Therefore, many of the requested details regarding acceptance criteria for AI performance, sample sizes for test/training sets, expert adjudication methods, MRMC studies, standalone AI performance, and ground truth establishment (which are typical for AI/ML medical devices) are not applicable to this submission. This document describes a new version of a thermal regulating system, primarily undergoing performance and biocompatibility testing.

Here's an attempt to answer the questions based only on the provided text, indicating when information is not applicable or not provided:

Acceptance Criteria and Device Performance (Based on Provided Text)

The document indicates that the device's performance was verified through specific tests. While explicit numeric acceptance criteria are not detailed in the provided excerpt, the "reported device performance" is a statement of passing these tests.

Study Details:

1. A table of acceptance criteria and the reported device performance:
   (See table above)

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

   • Sample Size: Not explicitly stated for performance or biocompatibility tests (e.g., how many gowns were tested). The text mentions "Device samples."
   • Data Provenance: Not specified (e.g., country of origin). The tests are described as "performance testing" and "biocompatibility testing." The nature of the device (a warming gown) suggests these would be laboratory-based tests on the device itself, rather than human subject data. The submission is from 3M Company, St. Paul, Minnesota, USA.

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

   • Not applicable. This submission focuses on physical device performance and biocompatibility, not an AI/ML diagnostic or prognostic tool requiring expert-established ground truth from patient data.

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

   • Not applicable. See point 3.

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. This is not an AI-assisted device. The study described is performance and biocompatibility testing of a physical medical device.

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

   • No. This is not an algorithm-only device. The study described is performance and biocompatibility testing of a physical medical device.

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

   • Objective Test Standards: The "ground truth" for this device's performance is based on the specified international standards and their criteria for passing (e.g., IEC-80601-2-35 for thermal performance, ISO 10993-1 for biocompatibility). These are objective, measurable outcomes determined through physical and chemical testing, not clinical consensus or patient outcomes in the sense of a clinical trial.

8. The sample size for the training set:

   • Not applicable. This type of device does not involve a "training set" in the context of machine learning.

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

   • Not applicable. See point 8.

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For thermal regulation of a patient's temperature to prevent hypothermia and/or reduce cold discomfort during and after surgical procedures. It is intended for use by appropriately trained healthcare professionals in clinical environments.

Snuggle Warm® Convective Warming Systems (SW-4000 and EQ-5000) that will include the proposed blankets (Model SW-2013, SW-2014R, SW-2015, SW-2016, SW-2018, SW-2018 & SW-2019). The Convective Warming Systems (SW-4000 and EQ-5000), are identified as a Thermal Regulation System by the FDA, consists of a Convective Warming Unit (temperature controller), a hose, and a single-use Convective Warming Blanket. The Convective Warming Systems' (SW-4000 and EQ-5000) single-use disposable Convective Warming Blankets are placed in contact with the patient and attached to a warming unit via a hose with hose-end temperature controls. The warming unit generates warm air that is distributed throughout the warming blanket to warm the patient during and after surgical procedures. It is intended for thermally regulating a patient's temperature to prevent hypothermia by a warm air heated blanket system to reduce cold discomfort during and after surgical procedures. The seven proposed blankets are single-use Convective Warming Blankets that will be added to the existing Convective Warming Blanket family. They are components of the Convective Warming Systems (SW-4000 and EQ-5000) of which the indications for use remain the same.

The provided text describes a 510(k) summary for several convective warming blankets. It does not contain information about acceptance criteria or a study proving performance against such criteria in the context of an AI/ML device.

However, based on the non-clinical data section, I can deduce the type of assessment performed. The device in question is a medical device (convective warming blankets), and its performance is evaluated against established standards and through comparison to predicate devices, rather than through an AI/ML model's statistical performance.

Therefore, many of the requested fields (sample size, experts, adjudication, MRMC, standalone, training set) are not applicable to this type of device submission.

Here's a breakdown of what can be extracted and what cannot:

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):

• Not applicable for this type of device. The assessment was based on bench testing and comparison of design and materials, not a "test set" of data in the context of an AI/ML device. The document explicitly states "Clinical Data: Not required."

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):

• Not applicable. No ground truth establishment by external experts for a test set is mentioned. The evaluation was internal bench testing and compliance with standards.

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

• Not applicable. No adjudication method for a test set is mentioned.

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 refers to AI/ML device studies, which this is not.

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

• Not applicable. This refers to AI/ML device studies, which this is not.

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

• Not applicable in the AI/ML sense. The "ground truth" here is compliance with engineering standards (ASTM F2196, ISO 10993-1) and demonstrated functional equivalence through bench testing to previously approved predicate devices.

8. The sample size for the training set:

• Not applicable. No training set is mentioned as this is not an AI/ML device.

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

• Not applicable. No training set is mentioned.

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