Is the Landing Error Scoring System Reliable and Valid? A Systematic Review
Bibliographic record
Abstract
CONTEXT: The Landing Error Scoring System (LESS) is a clinical tool often used in research and practice to identify athletes presenting high injury-risk biomechanical patterns during a jump-landing task. OBJECTIVE: To systematically review the literature addressing the psychometric properties of the LESS. DATA SOURCES: Three electronic databases (PubMed, Web of Science, and Scopus) were searched on March 28, 2018, using the term "Landing Error Scoring System." STUDY SELECTION: All studies using the LESS as main outcome measure and addressing its reliability, validity against motion capture system, and predictive validity were included. Original English-language studies published in peer-reviewed journals were reviewed. Studies using modified versions of the LESS were excluded. STUDY DESIGN: Systematic literature review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: Study design, population, LESS testing procedures, LESS scores, statistical analysis, and main results were extracted from studies using a standardized template. RESULTS: Ten studies met inclusion criteria and were appraised using Newcastle-Ottawa Quality Assessment Scale adapted for cross-sectional studies. The overall LESS score demonstrated good-to-excellent intrarater (intraclass correlation coefficient [ICC], 0.82-0.99), interrater (ICC, 0.83-0.92), and intersession reliability (ICC, 0.81). The validity of the overall LESS score against 3-dimensional jump-landing biomechanics was good when individuals were divided into 4 quartiles based on LESS scores. The validity of individual LESS items versus 3-dimensional motion capture data was moderate-to-excellent for most of the items addressing key risk factors for anterior cruciate ligament (ACL) injury. The predictive value of the LESS for ACL and other noncontact lower-extremity injuries remains uncertain based on the current scientific evidence. CONCLUSION: The LESS is a reliable screening tool. However, further work is needed to improve the LESS validity against motion capture system and confirm its predictive validity for ACL and other noncontact lower-extremity injuries.
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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 | 0.043 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".