Predictive Indicators of Overuse Injuries in Adolescent Endurance Athletes
Bibliographic record
Abstract
Purpose: To analyze the influence of training exposure and the utility of self-report questionnaires on predicting overuse injuries in adolescent endurance athletes. Methods: Five adolescent male endurance athletes (15.7 ± 1.4 y) from a full-time sports academy answered 2 questionnaires (Recovery Cue; RC-q and Oslo Sports Trauma Research questionnaire; OSTRC-q) on a weekly basis for 1 season (37 wk) to detect signs of overtraining and underrecovery (RC-q) and early symptoms of lower-limb injuries (OSTRC-q). All overuse injuries were retrospectively analyzed to detect which variations in the questionnaires in the weeks preceding injury were best associated. Overuse incidence rates were calculated based on training exposure. Results: Lower-limb overuse injuries accounted for 73% of total injuries. The incidence rate for overuse training-related injuries was 10 injuries/1000 h. Strong correlations were observed between individual running exposure and overuse injury incidence ( r 2 = .66), number of overuse injuries ( r 2 = .69), and days lost ( r 2 = .66). A change of 20% or more in the RC-q score in the preceding week was associated with 67% of the lower-limb overuse injuries. Musculoskeletal symptoms were only detected in advance by the OSTRC-q in 27% of the episodes. Conclusion: Training exposure (especially running exposure) was shown to be related to overuse injuries, suggesting that monitoring training load is a key factor for injury prevention. Worsening scores in the RC-q (but not the OSTRC) may be an indicator of overuse injury in adolescent endurance runners when used longitudinally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".