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The Universal Spinal Anterior Locking Plate System is intended for anterior/anterolateral fixation for the following indications:

• degenerative disc disease (DDD) (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies);
• spondylolisthesis;
• trauma (i.e., fracture or dislocation);
• spinal stenosis;
• curvatures (i.e., scoliosis, kyphosis, and/or lordosis);
• tumor;
• pseudoarthrosis;
• and failed previous fusion.
  This device is not intended for screw attachment or fixation to the posterior elements (pedicles) of the cervical, thoracic, or lumbar spine.

The USA Locking Plate System consists of a multiple sized plates and screws. All components are fabricated from titanium alloy (Ti-6Al-4V) that conforms to ASTM F136 Fixation is provided by inserting screws through holes in the plate into the vertebral bodies of the thoraco-lumbar and lumbar spine. The bone plates offer slotted and round holes that allow the bone screws to be located and securely seated in various positions. There are two plate configurations available: the straight locking plate and the narrow locking plate. The screws include the universal locking screw, the self locking screw and the cancellous locking screw.

The provided text describes a medical device, the "Universal Spinal Anterior Locking Plate System," and its 510(k) submission. However, the document does not contain information related to acceptance criteria, specific device performance numerical values, or details of a study that would prove the device meets such criteria.

The submission focuses on establishing substantial equivalence to a predicate device (Amset Locking Plate K905023) primarily based on design and indications, with the main difference being a change in material. It mentions "Engineering analyses show that the maximum allowable loads for the USA straight and narrow plates are greater than the predicate plate," but no specific loads, acceptance criteria, or a study report are provided.

Therefore, I cannot populate the table or answer most of the questions as the necessary information is not present in the provided text.

Here is what I can state based on the text:

1. 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)

• Not specified. The document mentions "Engineering analyses," which typically refers to mechanical testing or simulations, but no details on sample size or data provenance are provided.

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)

• Not applicable/Not specified. This type of information is usually relevant for studies involving human interpretation (e.g., imaging studies or diagnostic devices). This document describes a surgical implant, and the assessment is based on engineering analyses.

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

• Not applicable/Not specified. See point 3.

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 a diagnostic device involving human readers or AI assistance.

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

• No. This is not an algorithm-based device.

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

• Not explicitly stated in terms of "ground truth" for a clinical study. For mechanical performance, the "ground truth" would be established physical material properties and mechanical testing standards. The document only mentions "Engineering analyses."

8. The sample size for the training set

• Not applicable/Not specified. This device does not involve a "training set" in the context of machine learning or AI.

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

• Not applicable/Not specified. This device does not involve a "training set" or corresponding ground truth establishment in the context of machine learning or AI.

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The Window Cervical Plate System is intended for anterior interbody screw fixation of the cervical spine. The system is indicated for use in the temporary stabilization of the anterior spine during the development of cervical spinal fusions in patients with degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (defined as kyphosis, lordosis, or scoliosis), pseudoarthrosis, and /or failed previous fusions.

The Window Cervical Plate System is a set of implants designed to be implanted via an anterior approach to the cervical spine. The system includes various plates and screws manufactured from Titanium. The material used in the manufacture of the components in the Window Cervical System meets ASTM F136 for Ti 6A1-4VELI.

This is a 510(k) premarket notification for a medical device, not a study evaluating an AI algorithm. Therefore, the requested information about acceptance criteria, device performance, sample sizes, ground truth, expert qualifications, adjudication methods, and comparative effectiveness studies is mostly not applicable in the context of an AI/ML device.

However, I can extract the relevant information within the context of a medical device submission:

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

The submission does not present specific acceptance criteria in the form of performance metrics for the "Window Cervical Dynamic Plate System." Instead, the key "acceptance criteria" here are demonstrating substantial equivalence to a legally marketed predicate device.

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

This submission describes a medical device (surgical implant), not an AI algorithm evaluated on a 'test set' of data in the typical sense. Therefore, concepts like sample size for a test set, data provenance (country, retrospective/prospective), and ground truth are not directly applicable in the way they would be for an AI/ML development.

The "testing" mentioned refers to mechanical and material testing (e.g., ASTM F1717) of the physical device or its components to ensure it meets performance standards and can be considered equivalent to the predicate. The "sample size" for such tests would refer to the number of devices or components tested, not a dataset of patient information. This information is mentioned as being in "Appendix 1" but not detailed in the provided text.

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 is not applicable to this medical device submission. Ground truth, in the context of AI, refers to expert-labeled data. For a physical medical implant, the "truth" is established by engineering standards, material properties, and clinical outcomes over time (post-market surveillance), not by expert labeling of a dataset.

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

Not applicable. Adjudication methods are used to resolve disagreements among human reviewers labeling data for AI/ML training or testing. This process is not part of a 510(k) submission for a physical device.

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. MRMC studies are used to evaluate diagnostic imaging devices or AI tools that assist human readers. This device is a surgical implant; it does not involve human "readers" or AI assistance in interpretation.

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

Not applicable. This device is a physical surgical implant, not an algorithm.

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

Not applicable. The "truth" for this device revolves around its physical properties, biocompatibility, and mechanical performance, established through engineering tests and adherence to standards (like ASTM F136, ASTM F1717). Its clinical safety and effectiveness are inferred from its substantial equivalence to a predicate device with a history of safe use and similar design/materials.

8. The sample size for the training set

Not applicable. Training sets are used for AI/ML algorithms. This is a physical medical device.

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

Not applicable.

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The Triple-Fix Spinal System is a pedicle screw system intended to provide immobilization and stabilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities of the thoracic, lumbar, and sacral spine: degenerative spondylolithesis with objective evidence of neurologic impairment, fracture, dislocation, scoliosis, kyphosis, spinal turnor, and failed previous fusion (pseudoarthrosis).

The posterior Triple-Fix Spinal System is also indicated for pedicle screw fixation for severe spondylolithesis (grades 3 and 4) at L5-S1, in skeletally mature patients, when autogenous bone graft is used, when affixed to the posterior lumbosacral spine, and intended to be removed after solid fusion is attained. Levels of fixation are from L3-S1.

The posterior Triple-Fix System, when not used with pedicle screws is indicated for hook, wire, and /or sacral screw fixation from T1 to the ilium sacrum. The non-pedicle screw indications are spondylolithesis, degenerative disc disease, (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies), deformities (scoliosis, lordosis and kyphosis), tumor, fracture, and previous failed fusion surgery.

The Advanced Spine Technology Triple-Fix Spinal System is a system that is intended to be used for posterior lumbar fusion procedures. The system is manufactured from titanium which complies with ASTM F136. The components, which are included as part of the system, include screws, rods, plates, and accessory connection components.

The provided document does not contain information regarding acceptance criteria or a study proving the device meets acceptance criteria in the way typical for AI/ML device submissions (e.g., performance metrics against a defined standard). This 510(k) pertains to a spinal fixation system, which is a physical medical device, not an AI/ML algorithm.

Therefore, many of the requested categories are not applicable to traditional hardware device submissions like this one. The document focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific quantitative acceptance criteria through a clinical or algorithmic study.

Here's an analysis based on the information provided and a recognition of the device type:

Device Type: Spinal Fixation System (physical hardware)

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

• Product Components
• Indications for Use
• Materials
• Product Labeling
• Packaging/Sterilization | The Triple-Fix Spinal System is substantially equivalent to the Isola Spinal System and the Mirage Spinal System.
• Product Components: Screws, Connectors, Plates, Rods, and offset Connectors (similar to predicates).
• Indications for Use: Identical to predicates.
• Materials: Stainless Steel or Titanium (similar to predicates).
• Product Labeling: Instructions for use and box labeling including all necessary warning statements (similar to predicates).
• Packaging/Sterilization: Non-sterile, single use only (similar to predicates). |
  | Mechanical Performance:
• Meeting relevant ASTM standards for spinal implant systems (e.g., ASTM F1717) | Testing was completed as per ASTM F1717 in order to demonstrate equivalence. The results of testing are included in appendix 1 of the submission. (Specific results are not detailed in the provided text, but the statement indicates the testing was done and met the requirements to demonstrate equivalence). |
  | Biocompatibility: (Implied for implantable devices) | Manufactured from titanium which complies with ASTM F136 (a standard for wrought titanium-6aluminum-4vanadium alloy for surgical implant applications). This compliance suggests biocompatibility. |

2. Sample size used for the test set and the data provenance

• Sample Size: Not applicable in the context of an AI/ML test set. For mechanical testing (like ASTM F1717), a certain number of device samples would be tested, but the specific number is not provided in this summary.
• Data Provenance: Not applicable in the AI/ML sense. Mechanical testing data would be generated in a lab setting.

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

• Not applicable. This device is a physical implant, not an AI/ML diagnostic or prognostic tool requiring expert consensus for ground truth.

4. Adjudication method for the test set

• Not applicable.

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-assisted device.

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

• Not applicable. This is a physical device.

7. The type of ground truth used

• Not applicable in the AI/ML context. For a physical device, the "ground truth" for demonstrating safety and effectiveness relies on:
  • Validated engineering principles and materials standards: Such as compliance with ASTM F136 for materials and ASTM F1717 for mechanical testing.
  • Demonstration of substantial equivalence to legally marketed predicate devices: Which have established safety and effectiveness.

8. The sample size for the training set

• Not applicable. There is no AI/ML training set for this device.

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

• Not applicable. There is no AI/ML training set for this device.

Summary of Device Acceptance Philosophy (for this type of device):

The acceptance of the Triple-Fix Spinal Fixation System is based primarily on demonstrating substantial equivalence to existing legally marketed predicate devices (Isola Spinal System, Mirage Spinal System). This involves showing similar:

• Design and components
• Intended use and indications
• Materials of construction
• Labeling
• Packaging and sterilization

Crucially, the submission also references mechanical testing (ASTM F1717) to scientifically demonstrate that the device performs equivalently to or better than the predicate devices in terms of its structural integrity and functional requirements in a simulated environment. The "acceptance criteria" here are that the device meets the performance characteristics of its predicates and relevant engineering standards, thereby ensuring comparable safety and effectiveness.

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