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The Ellipse PRECICE System is indicated for limb lengthening of the tibia and femur.

The Ellipse PRECICE System is composed of the PRECICE nail (supplied sterile), locking screws, surgical instruments and an external remote controller (ERC, ERC2P). The nail is available in tibia or femur models with various diameters, lengths and screw hole configurations to accommodate a variety of patient anatomies. The locking screws are also available in a variety of diameters and lengths. The PRECICE nail is supplied sterile by gamma radiation while the locking screws and PRECICE specific accessories are supplied non-sterile and must be sterilized prior to use. The nail contains an enclosed rare earth magnet, telescoping lead screw/nut assembly, and planetary gearing.

This document is a 510(k) summary for the PRECICE® Intramedullary Limb Lengthening System. It describes the device and claims substantial equivalence to previously cleared devices. However, it does not contain the detailed acceptance criteria and study data typically found in a clinical study report or a more comprehensive premarket submission.

Based on the provided text, here's what can be extracted and what information is not available:

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

The document presents in vitro mechanical testing for the device modifications. It doesn't explicitly state "acceptance criteria" as numerical thresholds that were met, but rather implies that the modifications were found substantially equivalent through these tests.

Missing Information: Specific numerical acceptance criteria and the actual numerical results from these tests are not provided in this summary. The statement "meet the performance specifications" is a general conclusion without supporting data.

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

This document describes in vitro mechanical testing, not a clinical study involving human subjects. Therefore, traditional "sample size," "test set," and "data provenance" as applied to clinical data are not directly applicable.

Missing Information: The number of devices or components tested for Static Torsion and Tensile Strength is not specified.

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 document details in vitro mechanical testing. The concept of "ground truth" established by human experts is not relevant to this type of testing. Engineering standards (ASTM F1264) are used for evaluation.

Missing Information: Not applicable.

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

This is not applicable to in vitro mechanical testing.

Missing Information: 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

This document describes a medical device, an intramedullary limb lengthening system, not an AI or imaging diagnostic tool. Therefore, an MRMC study is not relevant.

Missing Information: Not applicable.

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

This is not applicable to an intramedullary limb lengthening system.

Missing Information: Not applicable.

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

For the in vitro testing, the "ground truth" would be the established engineering principles and standards (e.g., ASTM F1264) for evaluating the mechanical properties of a medical device.

Missing Information: While the standard is mentioned, specific numerical "ground truth" values for performance are not.

8. The sample size for the training set

This document describes in vitro mechanical testing for a physical medical device, not a machine learning model. Therefore, the concept of a "training set" is not applicable.

Missing Information: Not applicable.

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

Not applicable, as it's not a machine learning model.

Missing Information: Not applicable.

In summary: The provided text is a 510(k) summary focused on demonstrating substantial equivalence for modifications to an intramedullary limb lengthening system through in vitro mechanical testing. It does not contain the detailed clinical study data, ground truth establishment, or expert involvement typically associated with AI/diagnostic device evaluations. The reported "performance" is a general statement of meeting specifications rather than specific numerical results against defined acceptance criteria.

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The Ellipse PRECICE System is indicated for limb lengthening of the tibia and femur.

The Ellipse PRECICE System is composed of the PRECICE nail (supplied sterile), locking screws, surgical instruments and an external remote controller (ERC, ERC2P). The nail is available in tibia or femur models with various diameters, lengths and screwhole configurations to accommodate a variety of patient anatomies. The locking screws are also available in a variety of diameters and lengths. The PRECICE nail is supplied sterile by gamma radiation while the locking screws and PRECICE specific accessories are supplied non-sterile and must be sterilized prior to use. The nail contains an enclosed rare earth magnet, telescoping lead screw/nut assembly, and planetary gearing.

This document is a 510(k) premarket notification letter for the PRECICE® Intramedullary Limb Lengthening System and its associated 510(k) summary. It describes the device, its indications for use, and a demonstration of substantial equivalence to previously cleared predicate devices.

Here's an analysis of the provided text with respect to your specific questions:

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

The document does not explicitly state acceptance criteria in a quantitative table format alongside performance results. Instead, it refers to general findings of "substantial equivalence" based on "in vitro testing" and "FEA Simulations."

• Acceptance Criteria (Implied): The implied acceptance criteria are that the modified device's performance, as assessed by in vitro testing (tensile strength) and FEA simulations, is comparable to, and does not negatively impact, the safety and effectiveness demonstrated by the predicate devices. The phrase "meet the performance specifications" suggests that certain internal design specifications or established performance benchmarks were met, though these are not detailed.
• Reported Device Performance:
  • In Vitro Testing (Tensile Strength): The table lists "In Vitro Testing (Tensile Strength)" as a performed test. The conclusion section states "Conclusions can be drawn from the tests that the modifications to the PRECICE System are substantially equivalent and meet the performance specifications." This indicates that the tensile strength of the modified nails was found to be acceptable and comparable to the predicate. No specific numerical performance values (e.g., specific tensile strength in MPa) are provided.
  • FEA Simulations: The table lists "FEA Simulations" as a performed test. Similar to the in vitro testing, the conclusion states that these simulations supported substantial equivalence and met performance specifications. No specific numerical results of the simulations (e.g., stress distribution, deformation values) are provided.

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

The document does not provide a sample size for any test set. The tests mentioned are "In Vitro Testing (Tensile Strength)" and "FEA Simulations," which are likely conducted in a lab environment rather than using patient data. Therefore, questions of retrospective/prospective and country of origin are not applicable in this context.

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 question is not applicable to the information provided. The study mentioned involves in vitro testing and finite element analysis (FEA) simulations, which are engineering and mechanical assessments, not clinical studies requiring expert ground truth for diagnostic or prognostic purposes.

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

This question is not applicable. Adjudication methods are relevant for clinical studies where expert consensus is needed to establish ground truth or interpret results. The study described here is focused on mechanical and engineering performance.

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 mention of a multi-reader multi-case (MRMC) comparative effectiveness study, nor is there any indication that the device incorporates AI. This device is an intramedullary nail for limb lengthening, a mechanical implant.

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

This question is not applicable. The device is a physical medical implant (an intramedullary nail) and does not involve an algorithm working in a standalone capacity.

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

Since the "study" involves in vitro testing and FEA simulations, the "ground truth" would be established by the physical measurements from the in vitro tests (e.g., force-displacement curves for tensile strength) and the mathematical/computational results from the FEA simulations (e.g., stress and strain values calculated according to material properties and geometry). There is no "expert consensus," "pathology," or "outcomes data" ground truth in this type of engineering assessment.

8. The sample size for the training set

This question is not applicable. The device is a mechanical implant, and the assessment involves direct testing and simulation. There is no "training set" in the context of an algorithm or machine learning.

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

This question is not applicable for the same reasons as #8.

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