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510(k) Data Aggregation
(70 days)
PILLAR SA Ti Spacer System (82-XXX)
The PILLAR SA Ti Spacer System when used with screws is a standalone interbody implant indicated for use as an adjunct to fusion in skeletally mature patients with degenerative disc disease (DDD) at one or two contiguous levels from L2 to S1. DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. The DDD patients may also have up to Grade 1 spondylolisthesis at the involved level(s). Patients must have undergone a regimen of at least six (6) months of non-operative treatment prior to being treated with the device.
The interbody is intended to be used with autogenous bone graft and/or allogeneic bone graft composed of cancellous, cortical, and/or corticocancellous bone. The The PILLAR SA Ti Interbody may be used as a standalone device only when at least two Screws are inserted with one inferior and one superior screw trajectory. If the surgeon chooses to use the PILLAR SA Ti Interbody with fewer than two Screws inserted with one inferior and one superior screw trajectory, then additional supplemental fixation system must be used. Hyperlordotic sizes (greater than 20°) are intended for use with supplemental fixation.
The PILLAR SA Ti Spacer System is an integrated in ody fusion device for use in anterior lumbar interbody fusion (ALIF) procedures. The PILLAR SA TI Spacer System is compised of 3D printed titanium interbody spacers with porous titanium end plates and a functional gradient porous structure, and bone screws. The implants are offered in multiple footprints and lordotic options to accommodate individual patient anatomy. Each porous interbody has a large central window for graft material and a threaded hole with a zero-step locking mechanism for screw retention.
The PILLAR SA TI Spacer System implants are provided sterile.
PILLAR SA TI Spacer System implants are designed to be used with PILLAR SA TI Spacer System instrumentation and are not compatible with components from any other manufacturer's system.
This is a 510(k) premarket notification for a medical device, specifically an intervertebral body fusion device. These types of notifications typically focus on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting a detailed clinical study with acceptance criteria for device performance in the same way a diagnostic AI/ML device would.
Therefore, the provided text does not contain the kind of information requested for AI/ML device studies (e.g., acceptance criteria for performance metrics like sensitivity/specificity, sample sizes for test sets, expert adjudication, MRMC studies, standalone performance, training set details).
Instead, the "acceptance criteria" for this device are related to its mechanical performance and substantial equivalence to existing devices. The study proving it meets these criteria is a non-clinical mechanical testing study.
Here's how to interpret the provided text in the context of your request:
1. A table of acceptance criteria and the reported device performance:
Acceptance Criteria Category | Reported Device Performance Summary (from text) |
---|---|
Mechanical Performance | "adequately demonstrated that the PLLAR SA Ti Spacer System implants (device under review) tested as well or better than the cited predicate devices." |
Design/Materials | "the same: intent population, method of fixation, surgical approach, sterilization, materials and general design elements as the cited PIC Spacer System (K200052) and Meridian Interbody System (K233694) predicate devices." |
Indications for Use | "The subject PILLAR SA Ti Spacer System has the same intended use and comparable indications for use to the cited predicate devices." |
2. Sample size used for the test set and the data provenance:
- Sample Size: Not explicitly stated as a number of "patients" or "cases." The "sample size" here refers to the number of test specimens (implants) subjected to mechanical testing. This information is typically detailed in the full mechanical test reports, which are not included in this summary.
- Data Provenance: Not applicable in the context of clinical data. This is mechanical test data, likely generated in a laboratory setting by the manufacturer (Orthofix, Inc.).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. Ground truth for mechanical testing is established by engineering standards and measurements, not clinical expert consensus. The "experts" would be the engineers and technicians performing the tests and analyzing the results according to the specified ASTM standards.
4. Adjudication method for the test set:
- Not applicable. This is mechanical testing against predefined engineering standards (ASTM F2077, F2267, F1877), not expert adjudication of clinical findings.
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:
- No. This is not an AI/ML device, and no MRMC study was conducted.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- No. This is not an AI/ML device.
7. The type of ground truth used:
- Engineering Standards and Benchmarking: The "ground truth" for this device's performance is its ability to meet or exceed the mechanical performance characteristics of predicate devices, as evaluated according to industry standards (ASTM F2077, F2267, F1877).
8. The sample size for the training set:
- Not applicable. There is no "training set" as this is not an AI/ML device.
9. How the ground truth for the training set was established:
- Not applicable. There is no "training set."
In summary, the provided document is a 510(k) summary for a traditional medical device (intervertebral fusion device), not an AI/ML device. Therefore, the performance evaluation focuses on mechanical testing and substantial equivalence rather than clinical performance metrics and studies typical for AI/ML diagnostic tools.
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