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The WarmTouch Convective Warming System (warming unit and blanket) is intended for prevention and treatment of hypothermia, and for the management of appropriate normothermia.

The WarmTouch™ Convective Warming Unit is an electro-mechanical blower that delivers heated air. The WarmTouch™ Convective Warming Unit is part of the WarmTouch™ Convective Warming System that consists of the blower warming unit and Covidien's WarmTouch™ Blankets. The blower actively delivers heated air from the blower's flexible hose to a lightweight blanket. During this process, the blanket has already been draped over the patient's body, before the blanket begins distributing the heated air through numerous small perforations in the blanket, which allows the air to reach the targeted areas of the patient's body.

The provided text describes a 510(k) submission for the Covidien WarmTouch™ Convective Warming Unit. This submission focuses on modifications to an existing device, emphasizing electrical, mechanical, and software changes for compliance and improved performance. It is a modification to a previously cleared device, not a new AI/software-as-a-medical-device (SaMD) product, and therefore the traditional clinical study requirements for AI devices (such as those involving performance metrics like accuracy, sensitivity, specificity, MRMC studies, or training/test set details) are not applicable or detailed in this document.

The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to the design verification and validation testing performed to ensure the modified device still functions as intended and safely, similar to its predicate device. This is a common approach for 510(k) submissions focusing on device modifications.

Here's an interpretation based on the provided text, addressing the points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance & 6. Standalone Performance:

Based on the document, the "acceptance criteria" appear to be met through design verification and validation, demonstrating that the modified device performs comparably to the predicate device and meets its specified requirements. The "device performance" is described in terms of improved accuracy for alarms and improved air temperature performance.

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

• Test Set Sample Size: The document does not specify a quantitative sample size for a "test set" in the context of clinical data or patient cases. The testing described is primarily design verification and validation, and usability studies. For usability studies, a "user" group would constitute the "sample," but the number is not provided.
• Data Provenance: Not applicable in the context of a clinical test set. The data originates from internal engineering tests and usability studies conducted by Covidien.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. This type of information is typically not included in 510(k) submissions focused on engineering changes and substantial equivalence. Usability studies would involve "users," likely healthcare professionals, but their specific qualifications or roles in establishing "ground truth" (as it pertains to clinical outcomes) are not detailed.

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

• Adjudication Method: Not applicable. The "test set" in this context refers to engineering and usability tests, not clinical performance requiring expert adjudication of diagnoses or outcomes.

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:

• MRMC Study: No, an MRMC study was not done. This device is a convective warming unit, which is a physical device used to regulate patient temperature, not an AI or diagnostic imaging device that would involve human "readers" interpreting output.

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

• Type of Ground Truth: The "ground truth" for this device's performance is based on engineering specifications and compliance standards (e.g., IEC 80601-2-35:2009, RoHS Directive). For usability, satisfaction or performance against defined tasks by users would serve as the "ground truth." There is no mention of clinical outcomes data, pathology, or expert consensus on patient conditions.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable. This document describes a medical device (hardware with embedded software), not a machine learning model that would require a "training set."

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

• Ground Truth for Training Set: Not applicable, as there is no machine learning "training set" for this device.

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