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The Piccolo Composite Proximal Tibia Plate System is intended to treat fractures of the proximal tibia in adults and adolescents in which the growth plates have fused.

For Lateral Plates - non-unions, and factures including simple, comminuted, lateral wedge, depression, medial wedge, bicondylar combination of lateral wedge and depression, periprosthetic, and fractures with associated shaft fractures.

For Medial Plates - intended to buttress metaphyseal fractures of the medial tibia plateau, split-type fractures of the medial tibia plateau, medial split fractures with associated depression fractures of the medial tibia plateau. The plates may also be used for fixation of the proximal quarter (lateral and medial) of the tibia as well as segmental fractures of the proximal tibia.

The Piccolo Composite Proximal Tibia Plate System comprises implants (pre-contoured lateral and medial plates (in left and right configurations), and screws) in different dimensions, and instruments.

The Piccolo Composite proximal tibia plates are made of carbon fiber reinforced polyetheretherketone (CFR-PEEK) and incorporate tantalum markers. The screws are made of titanium alloy.

The provided text is a 510(k) summary for a medical device (Piccolo Composite Proximal Tibia Plate System) and focuses on demonstrating substantial equivalence to predicate devices, primarily through mechanical performance testing. It does not contain information about studies involving human readers, AI, or diagnostic performance metrics typically associated with acceptance criteria for such studies.

Therefore, many of the requested sections about acceptance criteria, human reader performance, AI, and ground truth establishment cannot be answered from the provided document.

Here's a breakdown of what can be extracted and what cannot:

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

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

This information is not provided in the document. The performance testing described is mechanical, not clinical, and thus terms like "test set" for human or AI performance or "data provenance" in this context are not applicable.

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 information is not provided. The "ground truth" in this context refers to engineering standards and comparisons to predicate devices, not expert human interpretation.

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

This information is not provided. Arbitration methods are typically used for clinical studies with human observers, which is not the type of study described.

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

There is no indication that an MRMC comparative effectiveness study was done. This document describes the mechanical testing of a bone plate system, not a diagnostic or AI-assisted device. Therefore, no information on AI assistance or human reader improvement is available.

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

There is no indication that a standalone algorithm performance study was done. The device is a physical bone plate system, not an algorithm.

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

The "ground truth" for the mechanical performance testing appears to be established engineering standards (ASTM F 382) and performance data from legally marketed predicate devices.

8. The sample size for the training set

This information is not provided. The concept of a "training set" is not applicable to the mechanical performance testing described for this physical device.

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

This information is not provided. The concept of a "training set" and its ground truth is not applicable to the mechanical performance testing described for this physical device.

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CarboClear Cervical Plate System is intended for anterior fixation of the cervical spine.

The system is indicated for use in temporary stabilization of the anterior spine during the development of cervical spine fusion in patients with the following:

Degenerative disc disease (DDD) (as identified by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), spondylolisthesis, trauma (i.e., fractures or dislocations), spinal stenosis and tumors, deformity (e.g., scoliosis, kyphosis, lordosis), pseudoarthrosis, failed previous fusions.

The CarboClear Cervical Plate System comprises implants (plates and screws) in different dimensions, and instruments.

The CarboClear cervical plates are made of carbon fiber reinforced polyetheretherketone (CFR-PEEK) and may incorporate tantalum markers. The screws are made of titanium alloy.

This is a 510(k) premarket notification for a medical device, specifically the CarboClear® Cervical Plate System. These types of submissions typically focus on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific acceptance criteria in the way a diagnostic AI/ML device would.

Therefore, the provided document does not contain the information requested in points 1-9 regarding acceptance criteria and a study proving a device meets those criteria, particularly in the context of an AI/ML powered diagnostic or prognostic device.

The document discusses the following:

• Device Name: CarboClear® Cervical Plate System
• Intended Use: Anterior fixation of the cervical spine for temporary stabilization during fusion development in patients with various spinal conditions.
• Materials: Carbon fiber reinforced PEEK for plates, titanium alloy for screws.
• Predicate Devices: Several predicate devices are listed, including Spider® Cervical Plating System and Anterior CSLP System.
• Substantial Equivalence: The submission claims substantial equivalence based on intended use, design, materials, technological characteristics, principles of operation, and mechanical performance.
• Performance Testing: Static and dynamic compression bending, static torsion (per ASTM F1717), and static screw pushout testing were conducted, and the results were found to be comparable to predicate devices. Bacterial endotoxin evaluation was also performed.

To answer your specific questions, based only on the provided text, I must state that the requested information is not present:

1. A table of acceptance criteria and the reported device performance: Not provided. The document states "Performance characteristics... are comparable to those of predicate devices," but no specific acceptance criteria or quantitative performance data are given.
2. Sample size used for the test set and the data provenance: Not applicable or provided. This is physical device testing, not a data-driven AI/ML study.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable or provided. Ground truth in this context would likely refer to engineering specifications and measurements.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable or provided.
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: Not applicable. This is not an AI/ML diagnostic device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI/ML device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not directly specified in terms of "ground truth" for a diagnostic study. Mechanical performance is compared against predicate devices and ASTM standards.
8. The sample size for the training set: Not applicable. This is not an AI/ML device with a training set.
9. How the ground truth for the training set was established: Not applicable. This is not an AI/ML device with a training set.

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