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The coflex-IF is a posterior, non-pedicle supplemental fixation device intended for use with an interbody cage as an adjunct to fusion at a single level in the lumbar spine (L1 - S1). It is intended for attachment to the spinous processes for the purpose of achieving stabilization to promote fusion in patients with disc disease - defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies - with up to Grade 1 spondylolisthesis.

The coflex-IF system is an implant system for interspinous fixation within lumbar interbody fusion surgery. It consists of a single, U-shaped component, fabricated from medical grade titanium alloy (Ti6Al4V). A set of two wings extends vertically from the superior long arm of the device, with a second set of wings extending below the inferior long arm. A screw and sleeve are inserted through a prepared hole and fixes the crimped wings to the superior and inferior spinous processes.

The purpose of this Special 510(k) is to add a notch in the posterior portion of the U-shaped body. The modifications do not affect the intended use of the device or alter the fundamental scientific technology of the device.

Here's a breakdown of the acceptance criteria and study information for the coflex-IF device, based on the provided FDA 510(k) summary:

• Note: This document is a 510(k) summary for a spinal implant (coflex-IF), which is a physical medical device, not a software or AI-based diagnostic device. Therefore, many of the requested categories (like sample size for test/training sets, experts, adjudication methods, MRMC studies, standalone algorithm performance, and ground truth types for AI models) are not applicable to this type of submission. The information provided focuses on engineering and mechanical testing for substantial equivalence to a predicate device.

Acceptance Criteria and Reported Device Performance

Study Information (Where applicable, based on a physical medical device)

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

   • Sample Size: Not explicitly stated as "sample size" in the context of human subjects or data, but "The worst case device" was tested. This refers to a manufactured physical device chosen as representative of the most challenging conditions or configurations.
   • Data Provenance: Not applicable in the context of human data. The testing is described as mechanical performance testing of the device itself.

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

   • Not applicable. Ground truth for a physical device's mechanical performance is established through standardized engineering tests (e.g., ASTM F 1717-13) and comparison to the predicate device, not expert consensus on human data.

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

   • Not applicable. Mechanical testing results are objective measurements of physical properties, not subject to human adjudication in the way clinical diagnostic interpretations would be.

4. 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 is a physical spinal implant, not an AI software/diagnostic device.

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

   • Not applicable. This is a physical spinal implant, not an AI algorithm.

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

   • For mechanical performance, the "ground truth" is defined by the standards set by ASTM F 1717-13 and the established performance of the predicate device. The goal is substantial equivalence, meaning the new device performs acceptably within the same safety and effectiveness parameters as the legally marketed predicate.

7. The sample size for the training set:

   • Not applicable. This is a physical device, not an AI model requiring a training set of data. Device design and manufacturing parameters are based on engineering principles and material science, not machine learning.

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

   • Not applicable. As above, there is no AI training set for this type of device.

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