No association between knee biomechanics during 90-degree cutting maneuver and future anterior cruciate ligament injury in female floorball players
Bibliographic record
Abstract
BACKGROUND: Young female floorball players are among the athletes at the highest risk of rupturing their anterior cruciate ligament in games and practices. Most anterior cruciate ligament injuries in female floorball occur in noncontact cutting situations. The primary aim of this study was to investigate association between knee biomechanics during a 90-degree cutting task and the risk for future non-contact anterior cruciate ligament injury in female floorball players. METHODS: Sixty-five female floorball players (median age 19.5 years; range 15-31) completed preseason testing and 2.5-year follow-up. The testing included marker-based three-dimensional motion analysis of a floorball-specific 90-degree cutting task. Players' movements were recorded by eight 300 Hz cameras, and ground reaction forces were captured by 1500 Hz force plates. Seven biomechanical variables were measured: knee valgus angle at initial contact, peak knee valgus angle, knee flexion angle at initial contact, peak knee flexion angle, peak knee abduction moment, peak knee flexion moment, and peak knee internal rotation moment. Anterior cruciate ligament injuries were recorded throughout the follow-up. FINDINGS: Ten ACL injuries occurred and were included in the analysis. No differences were observed in baseline biomechanical variables between players who sustained an ACL injury during follow-up and those who remained injury-free. INTERPRETATION: Knee valgus and flexion angles, and knee abduction, flexion, and internal rotation moments during the 90-degree cutting test were not associated with the risk of non-contact anterior cruciate ligament injury in female floorball players. The 90-degree cutting test developed for this study cannot predict ACL injuries in female floorball players.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".