Passing return‐to‐sport criteria and landing biomechanics in young athletes following anterior cruciate ligament reconstruction
Bibliographic record
Abstract
We sought to evaluate the sagittal plane knee joint loading patterns during a double-leg landing task among young athletes who passed or failed return-to-sport (RTS) criteria following anterior cruciate ligament reconstruction reconstruction (ACLR), and in uninjured athletes. Participants completed quadriceps strength testing, a hop test battery, and the International Knee Documentation Committee subjective form following medical RTS clearance. ACLR participants "passed" RTS criteria (RTS-PASS) if they met ≥90 limb symmetry (%) or score on all measures and were categorized as "failing" (RTS-FAIL) if not. All participants completed three-dimensional motion analysis testing. Sagittal plane kinematic and kinetic variables were calculated during a double-leg drop vertical jump task. Mean limb values and limb symmetry indices (LSI; %) were calculated and compared using a one-way analysis of variance (ANOVA) (for LSI) and mixed between-within ANOVA (for group × limb differences). A total of 205 participants were included, with 39 in the RTS-PASS group, 109 in the RTS-FAIL group, and 57 control groups (CTRLs). The RTS-FAIL group demonstrated lower symmetry values for peak vertical ground reaction force, peak internal knee extension moment, and peak knee flexion angle. Group × limb interactions were observed for peak vertical ground reaction force and peak internal knee extension moment. Involved limb values were reduced in the RTS-PASS and RTS-FAIL groups compared to CTRLs, while the RTS-PASS groups had lower uninvolved limb values compared to the RTS-FAIL and CTRLs. Clinical Significance: Young athletes who pass RTS criteria after ACLR land symmetrically during a double-leg task, but symmetry was achieved by reducing loading on both limbs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".