PREDICTING LOWER EXTREMITY INJURY RISK IN SPORT THROUGH MOVEMENT QUALITY SCREENING: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background The identification of risk factors for lower extremity musculoskeletal (MSK) injury in sport is required to inform primary and secondary injury prevention strategies. Objective To determine whether measures of poor movement quality are associated with lower extremity MSK injury in sport. Design Systematic Review. Methods Five electronic databases (Medline, EMBASE, CINAHL, Sport Discus, SCOPUS) were systematically searched using keywords and Medical Sub-heading terms. Studies selected included: English language; original data; prospective analytic design; a rating of movement compensation, asymmetry, impairment or efficiency through composite batteries and/or individual tests; and participants with lower extremity MSK injury sustained with sport participation. PRISMA guidelines were followed and two independent raters assessed study quality [Downs and Black (DB) criteria] and level of evidence (Oxford Centre of Evidence-Based Medicine model). Results Of 4361 potential relevant studies, 13 were included. The majority (11/13) of the studies were low quality cohort studies (level 4 evidence). Median DB score was 11/33 (range 3–14). Heterogeneity in methodology and injury definition precluded meta-analyses. The Functional Movement Screen (FMS) was the most common movement quality outcome investigated (11/13 studies). Two studies considered interrelationships between risk factors, five reported diagnostic accuracy and none evaluated an intervention program targeting individuals identified as high-risk. There is inconsistent evidence that poor movement quality is associated with increased risk of lower extremity injury in sport. Conclusions There is insufficient evidence for widespread adoption of movement quality screening programs for predicting lower-extremity injury in sport. Future research should aim to identify the most relevant movement quality outcomes for predicting injury risk through high quality cohort studies. This should be followed by development and evaluation of pre-participation screening and lower extremity injury prevention programs through high quality randomized controlled trials targeting individuals at the greatest risk based on screening tests with validated test properties.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".