Drop landing between subjects post anterior cruciate ligament reconstruction and uninjured controls: A biomechanical and neuromuscular analysis
Bibliographic record
Abstract
BACKGROUND: The capacity of functional testing to identify 'at-risk' landing strategies in return to sport evaluation has been questioned. The purpose was to evaluate if biomechanical and muscle activation patterns differ during a drop landing task in subjects post anterior cruciate ligament reconstruction against uninjured controls. METHODS: 22 subjects within two years of anterior cruciate ligament reconstruction who had returned to sport and 25 activity matched controls were recruited. Sagittal plane knee and hip biomechanics and muscle activation amplitudes were recorded during a single leg drop landing task. Discrete measures derived from waveforms were analysed using t-tests and Analysis of Variance models (α=0.05). FINDINGS: Subjects post anterior cruciate ligament reconstruction displayed reduced knee (Effect Size = 1.2) and hip (Effect Size = 0.995) range of motion as well as reduced knee extension moments (Effect Size = 0.955) but exhibited no differences in quadriceps or hamstrings activation amplitudes versus uninjured controls. INTERPRETATION: Adoption of reduced knee and hip excursion or "stiffened" landing pattern despite similarities in muscle activation suggests the presence of adaptive strategies reflecting incomplete recovery despite receiving clearance for returned to sport. This suggests presence of altered patterns of movement which may place at increased risk for reinjury.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".