Validation of a magnetic resonance imaging based method to study passive knee laxity: An in-situ study
Bibliographic record
Abstract
Knee laxity can be described as an increased anterior tibial translation (ATT) or decreased stiffness of the tibiofemoral joint under an applied force. Küpper et al. (2013, 2016) and Westover et al. (2016) previously developed and reported on a magnetic resonance (MR)-based in vivo measure of knee laxity. In this study, the application of an in situ knee loading apparatus (ISKLA) is presented as a step toward validating the MR-based methodology for measuring ATT and stiffness. The ISKLA is designed to measure these outcome variables using MR imaging and is validated against a gold-standard ElectroForce mechanical test instrument (TA Instruments 3550). Accuracy was assessed through an in situ experimental setup by testing four cadaveric specimens with both the MR-based methodology and in the ElectroForce system. The outcome of the current study showed that the MR-based ATTs and stiffness measurements using the ISKLA were within 1.44-2.10 mm and 0.16-6.14 N/mm, respectively, of the corresponding values measured by the gold standard system. An excellent ICC was observed for ATT (0.97) and good ICC for stiffness (0.87) between the MR and ElectroForce-based systems across all target force levels. These findings suggest that the MR-based approach can be used with satisfactory accuracy and correlation to the gold standard measure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".