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The Smith & Nephew LEVELERT II Fluid Level Sensor is intended to weigh a bag of irrigation fluid and signal operating room personnel when a pre-determined amount of fluid remains.

The Levelert II is a irrigation fluid bag level sensing device designed to provide the user with fluid level feedback at pre-determined values. The pre-determined values are selected by the user. The Levelert II is a stand-alone DC battery powered, nonrechargeable device that is used in arthroscopic procedures.

The provided text describes the Levelert II Fluid Level Sensor. However, it does not contain specific acceptance criteria or detailed study data as would be found in a comprehensive clinical or performance validation study report.

The text is a 510(k) summary for a medical device submission to the FDA, which focuses on demonstrating substantial equivalence to a predicate device. It briefly mentions "performance testing," "EMI/EMC testing," "UL Safety testing," and "software verification and validation," but does not provide the results of these tests, the acceptance criteria, or the methodology.

Therefore, I cannot populate all sections of your request based solely on the provided text.

Here's an attempt to answer based on the available information, with significant gaps noted:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified in the provided text. The text refers to "performance testing" but does not detail the number of units or test conditions.
• Data Provenance: Not specified. It can be inferred that testing was conducted by or on behalf of Smith & Nephew, Inc., Endoscopy Division, likely in the USA. The type of study (retrospective or prospective) is not applicable here as this is device performance testing rather than a clinical study with patient data.

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

• Not applicable/Not specified. For a device like a fluid level sensor, ground truth for performance testing would typically be established by calibrated measurement tools (e.g., precise scales for weight, or volumetric measurements) rather than human expert interpretation.

4. Adjudication Method for the Test Set

• Not applicable/Not specified. Adjudication methods (like 2+1, 3+1) are typically used for studies involving human interpretation or subjective assessment, which is not the primary focus of performance testing for this type of device.

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

• No, an MRMC comparative effectiveness study was not mentioned. This type of study is more relevant for diagnostic imaging or interpretation tasks where human readers are involved. The Levelert II is a sensor, not an interpretive aid.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, the performance testing described ("performance testing, EMI/EMC testing, UL Safety testing and the software verification and validation") appears to be standalone algorithm/device performance testing. The device is a "stand-alone DC battery powered" unit, indicating its primary function is independent. The stated "signal operating room personnel" suggests an output for human users, but the core performance evaluation would be of the device's ability to accurately weigh and signal.

7. The Type of Ground Truth Used

• Not explicitly stated, but for a fluid level sensor, the ground truth would most likely be objective, quantitative measurements obtained through calibrated instruments (e.g., highly accurate scales to measure fluid weight, or precise volumetric measurements). It would not be expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

• Not applicable/Not specified. This device is described as having a "load cell instead of a spring loaded circuit design" and incorporating a "microprocessor internally." While microprocessors contain software/firmware developed through engineering processes, the text does not indicate a machine learning model that would require a "training set" in the common sense (i.e., for AI/ML). The term "software verification and validation" is used, which refers to standard software development lifecycle testing.

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

• Not applicable, as a "training set" for AI/ML is not indicated for this device based on the provided description. Software verification and validation would establish ground truth through functional specifications and test cases derived from engineering requirements.

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