Identification of good candidates for valgus bracing as a treatment for medial knee osteoarthritis
Bibliographic record
Abstract
Valgus unloader braces are a conservative treatment option for medial compartment knee osteoarthritis that aim to unload the damaged medial compartment through application of an external abduction moment. Patient response to bracing is highly variable, however. While some experience improvements in pain, function, and joint loading, others receive little to no benefit. The objective of this work was to analyze clinical measures and biomechanical characteristics of unbraced walking to identify variables that are associated with the mechanical effectiveness of valgus unloader bracing. Seventeen patients with medial knee osteoarthritis walked overground with and without a valgus unloader brace. A musculoskeletal model was used to estimate the contact forces in the medial compartment of the tibiofemoral joint and brace effectiveness was defined as the decrease in peak medial contact force between unbraced and braced conditions. Stepwise linear regression was used to identify clinical and biomechanical measures that predicted brace effectiveness. The final regression model explained 77% of the variance in brace effectiveness using two variables. Bracing was more effective for those with greater peak external hip adduction moments and for those with higher Kellgren-Lawrence grades, indicating more severe radiographic osteoarthritis. The hip adduction moment was the best predictor of brace effectiveness and was well correlated with several other measures indicating that it may be functioning as a "biomarker" for good bracing candidates. CLINICAL SIGNIFICANCE: The ability to predict good candidates for valgus bracing may improve issues of patient compliance and could enable the ability to train patients to respond better to bracing. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:351-356, 2018.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".