The association between knee muscle performance and clinical outcomes of knee function 1–4 years after a sport-related knee joint injury
Bibliographic record
Abstract
Objective Estimate the association between index leg knee muscle strength and rate of torque development (RTD), and self-reported and performance-based (i.e., hop) knee function in persons 1–4 years after a sport-related knee joint injury. Methods Data were collected at baseline of a clinical trial . Assessments included the Knee injury and Osteoarthritis Outcome Score Sport & Recreation subscale (KOOS sport ), 6-m timed hop (TH), and peak concentric isokinetic knee extensor and flexor torque and isometric RTD. Associations between peak torque and RTD with KOOS sport and TH were assessed using multivariable regression with nonlinear transformations. Results 53 participants (64.2% female) were included. Knee extensor peak torque was nonlinearly related to TH time, with a strong inverse relationship at lower torque values that changed as torque increased. Results were inconsistent for flexor peak torque, extensor RTD and flexor RTD, with inconsistencies in relationship shape and estimates of association between primary and sensitivity analyses. There was no association between strength/RTD and KOOS sport . Conclusion There was a nonlinear relationship between knee extensor strength and hop function, with lower strength being associated with a stronger relationship. As strength values increased, the relationship attenuated. Knee extensor and flexor strength, or RTD, were not associated with self-reported function.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".