Does the one-leg rise test reflect quadriceps strength in individuals following anterior cruciate ligament reconstruction?
Bibliographic record
Abstract
To explore if one-leg rise test performance is associated with quadriceps strength following anterior cruciate ligament reconstruction (ACLR). Cross-sectional. 100 individuals (50 females, 50 males) aged 18–40 years, 9–36 months post-ACLR with ongoing knee symptoms (KOOS4 <80/100). Number of one-leg rise repetitions (using an adjustable-height plinth) and isometric quadriceps strength using isokinetic dynamometry (60° flexion, normalised to body mass). Multivariable fractional polynomial regression models adjusted for sex explored relationships between one-leg rise performance (repetitions) and quadriceps strength (Nm/kg) for each limb. A non-linear, increasing association between one-leg rise performance and quadriceps strength was observed, with the rate of increase attenuating at higher values of one-leg rise performance. Similar relationships were observed in the ACLR (β = 0.15, 95%CI 0.10 to 0.20; adjusted r2 = 0.51) and contralateral limb (β = 0.14, 95%CI 0.08 to 0.19; r2 = 0.42). The one-leg rise test can be an indicator of quadriceps strength in individuals after ACLR, enabling clinicians to easily monitor quadriceps strength recovery without specialised equipment. With the relationship between one-leg rise performance and quadriceps strength attenuating with a larger number of one-leg rises achieved, other factors (e.g., motivation, endurance) likely contribute to one-leg rise performance at higher values.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".