Agreement and validity of observational risk screening guidelines in evaluation ACL injury risk factors
Bibliographic record
Abstract
Study Design: Methodological study.\nObjectives: To examine the agreement and validity of using observational risk screening guidelines to evaluate ACL injury risk factors.\nBackground: Post-pubescent females have an increased risk of anterior cruciate ligament (ACL) injury compared with their male counterparts partly due to their high-risk landing and cutting strategies. There are currently no scientifically-tested methods to screen for these high risk strategies in the clinic or on the field.\nMethods and Measures: Three physiotherapists used observational risk screening guidelines to rate the neuromuscular characteristics of 40 adolescent female soccer players. Drop jumps were rated as high risk or low risk based on the degree of knee abduction. Side hops and side cuts were rated on the degree of lower limb 'reaching'. Ratings were evaluated for intrarater and interrater agreement using kappa coefficients.3D motion analysis was used as a gold standard for determining the validity of ratings.\nResults: Acceptable intrarater and interrater agreement (k^0.61) were attained for the drop jump and the side hop, with kappa coefficients ranging from 0.64 to 0.94. Acceptable sensitivity (^0.80) was attained for the side hop and the side cut, with values ranging from 0.88 to 1.00. Acceptable specificity (^0.50) was attained for the drop jump, with values ranging from 0.64 to 0.72.\nConclusion: Observational risk screening is a practical and cost-effective method of screening for ACL injury risk. Based on levels of agreement and sensitivity, the side hop appears to be a suitable screening task. Agreement was acceptable for the drop jump but its validity needs further investigation.
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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 | 0.471 | 0.651 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".