Maturation and biomechanical risk factors associated with anterior cruciate ligament injury: Is there a link? A systematic review
Bibliographic record
Abstract
OBJECTIVE: To establish the potential link between sex-specific maturation and biomechanical factors associated with ACL injury during dynamic tasks. DESIGN: Systematic review. LITERATURE SEARCH: Five databases (CINHAL®, Cochrane Library, PubMed®, Scopus®, and SPORTDiscus) were searched and monitored until 27 May 2024. STUDY SELECTION CRITERIA: Cross-sectional, cohort, case-control, or interventional studies reporting one or more biomechanical variable linked with ACL injury and which assessed participants across two or more maturation phases were considered eligible. DATA SYNTHESIS: Studies were assessed for risk of bias using a modified version of the Newcastle Ottawa Scale and overall quality of evidence was rated using GRADE. Metrics and effect sizes were presented where available. RESULTS: Eighteen included studies examined 400 males, 1377 females, and 315 participants of undefined sex across various maturation phases. The methodological quality of most studies (n = 16) was considered good, and satisfactory for two. Knee abduction angle, knee abduction moment, knee flexion angle, and ground reaction forces were most commonly reported. Knee abduction angles and moments and knee flexion angles were greater in late and post-pubertal females than males and pre-pubertal females during both landing and cutting tasks. When normalised for body mass, ground reaction forces were generally greater in males compared to females overall and for less mature participants for both sexes. Overall quality of evidence was low or medium across the four biomechanical measures. CONCLUSION: Sex-specific maturation considerations are important in the targeted development and implementation of ACL injury risk identification and prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".