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The MicroFlow Spacer (Frontal) is indicated for use as a postoperative spacer to maintain an opening to the frontal sinuses within the first 14 days following surgery. The MicroFlow Spacer also helps to prevent obstruction.
The MicroFlow Spacer (Ethmoid) is indicated for use as a postoperative spacer to maintain an opening to the ethmoid sinuses within the first 14 days following surgery. The MicroFlow Spacer also helps to prevent obstruction.

The MicroFlow Spacer is a device that maintains an opening at the target sinus for up to 14 days postoperatively. There are two models of the MicroFlow Spacer: Ethmoid and Frontal. The modified device can be manually removed during an office follow-up visit at any time.

The provided material describes the Relieva Stratus MicroFlow Spacer, a manual surgical instrument. Because this is a physical medical device and not an AI/ML-driven solution, many of the typical acceptance criteria and study components related to software performance (such as sample sizes for test sets, expert ground truth establishment, MRMC studies, standalone performance, and training set details) are not applicable.

The document indicates that the device's performance was evaluated for substantial equivalence to a predicate device, the Relieva Stratus MicroFlow Spacer (K083574).

Here's a breakdown of the relevant information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document explicitly states: "The MicroFlow Spacer met all performance acceptance criteria." However, it does not provide a detailed table listing specific acceptance criteria and their corresponding reported performance values. It only offers a general statement of compliance. The focus of this 510(k) summary is on establishing substantial equivalence through relevant performance tests, rather than detailing a specific set of clinical performance metrics.

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

• Sample Size: Not specified. The document refers to "relevant performance tests" but does not detail the size or nature of any test sets.
• Data Provenance: Not specified. As this is a physical device submission focused on substantial equivalence to a predicate, the "data" likely refers to engineering performance testing rather than clinical study data from a specific country or a retrospective/prospective design.

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

This is not applicable as the device is a physical surgical instrument, not an AI/ML system requiring expert-adjudicated ground truth for software performance evaluation. The "ground truth" would relate to engineering specifications and clinical outcomes, likely assessed through standard medical device testing and clinical review, not expert consensus on image interpretation.

4. Adjudication Method for the Test Set:

This is not applicable for the reasons stated above.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study:

This is not applicable as the device is a physical surgical instrument, not an AI/ML system intended to assist human readers with interpretation.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study:

This is not applicable as the device is a physical surgical instrument, not a standalone algorithm.

7. Type of Ground Truth Used:

The "ground truth" for this medical device submission would be based on engineering specifications and potentially clinical outcomes reported in existing literature or previous predicate device data, rather than expert consensus, pathology, or outcomes data specifically generated for a new AI model's ground truth. The primary basis for approval is substantial equivalence to a legally marketed predicate device.

8. Sample Size for the Training Set:

This is not applicable as the device is a physical surgical instrument and does not involve an AI/ML training set.

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

This is not applicable as the device is a physical surgical instrument and does not involve an AI/ML training set.

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