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The Arctic Sun® Temperature Management System is a thermal regulating system, indicated for monitoring and controlling patient temperature in adult and pediatric patients of all ages.

The Arctic Sun Temperature Management System is a non-invasive, thermal regulating system that monitors and controls patient temperature within a range of 32°C to 38.5°C (89.6°F to 101.3°F). The Arctic Sun Temperature Management System consists of the Arctic Sun 5000 Control Module and disposable non-sterile ArcticGel Pads. The control module recirculates temperature-controlled water to the ArcticGel Pads. A commercially-available medical temperature probe, such as naso-pharyngeal, bladder, rectal, or esophageal, connected to the control module senses the patient's core temperature. A control algorithm automatically adjusts the water temperature (automatic mode) or the clinician can adjust the water temperature (manual mode) to obtain the desired patient temperature. The ArcticGel Pads come in various sizes to cover a broad range of patients and fit both males and females. Each pad has an inlet and an outlet connection that attaches to a fluid delivery line that is connected to the Arctic Sun 5000 Control Module. Up to six pads can be connected at one time. The pads adhere to the patient by the use of a biocompatible hydrogel adhesive.

This document describes a 510(k) premarket notification for the Arctic Sun Temperature Management System. It's a submission for modifications to an existing device, rather than a completely new one. Therefore, the information provided focuses on demonstrating substantial equivalence to a predicate device, rather than a standalone study proving efficacy against specific acceptance criteria in the same way one might for a new AI diagnostic tool.

Here's an analysis of the provided text in relation to your questions:

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

The document does not explicitly state quantitative acceptance criteria in a typical "table" format with pass/fail metrics. This is common for 510(k) submissions of modified devices where the primary goal is demonstrating substantial equivalence to a predicate. The performance testing is geared towards verifying the functionality of the modifications and ensuring the device still performs as intended and complies with relevant standards.

The document states:

• "Hardware design verification and software validation of the Arctic Sun Temperature Management System was performed to verify all new system and software requirements, the result of which confirmed the function of the device modifications."
• "In addition to functional testing, the Arctic Sun Temperature Management System was retested, when applicable to confirm ongoing compliance with Electrical Safety and Electromagnetic Compatibility Standards."
• "The Arctic Sun Temperature Management System performs as intended, raises no new or different safety or effectiveness issues and is substantially equivalent to the predicate device."

Therefore, the implied acceptance criteria are:

• Successful verification of all new system and software requirements.
• Confirmation of the function of the device modifications.
• Ongoing compliance with Electrical Safety Standards.
• Ongoing compliance with Electromagnetic Compatibility Standards.
• No new or different safety or effectiveness issues compared to the predicate device.
• Substantial equivalence to the predicate device in indications for use, design, technological characteristics, materials, and system features and functions.

Reported Device Performance:
The document states that these criteria were met, but it does not provide specific numerical results or raw data from the tests. It concludes that the device "performs as intended" and is "substantially equivalent."

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 does not mention "sample size" in the context of a dataset for model evaluation, as this is not an AI diagnostic device. The testing described refers to "functional testing" and "retesting" for compliance. These tests would involve physical device testing, software testing, and hardware verification, not the processing of a dataset of patient information. Therefore, data provenance is not applicable in this context.

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 is not applicable. The device is a "Thermal Regulating System" for monitoring and controlling patient temperature. Its performance is evaluated through engineering verification and validation against design specifications and relevant standards, not by comparing its outputs to expert-established ground truth on a test set (like an imaging AI might).

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

This is not applicable for the reasons stated in 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

This is not applicable. The Arctic Sun Temperature Management System is not an AI diagnostic tool used by human readers. It's a system to regulate patient temperature.

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

The device itself is a "Thermal Regulating System" with a control algorithm. The software validation confirmed the "function of the device modifications," which includes changes to the alert/alarm system, customizable protocols, data output for EMR, system default changes, and user preference changes to the user interface. While functional testing of the software components was performed, this isn't strictly a "standalone algorithm performance" study in the sense of evaluating an AI model's diagnostic accuracy. It's about verifying that the system operates correctly according to its specifications.

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

For this type of device, ground truth would be established by:

• Design Specifications: The intended behavior and performance limits defined during the device's design.
• Regulatory Standards: Compliance with electrical safety (e.g., IEC 60601-1) and electromagnetic compatibility (e.g., IEC 60601-1-2) standards.
• Predicate Device Performance: The established safety and effectiveness of the previously cleared Arctic Sun Temperature Management System (K142702 and K002577 for pads).
  The ground truth isn't in the form of clinical outcomes or expert consensus on patient data, but rather engineering and regulatory compliance.

8. The sample size for the training set

This is not applicable. The document describes a software and hardware modification verification and validation, not a machine learning model's training process.

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

This is not applicable for the reasons stated in point 8.

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