The SMS, Phone, and medical Examination sports injury surveillance system is a feasible and valid approach to measuring handball exposure, injury occurrence, and consequences in elite youth sport
Bibliographic record
Abstract
Current methods of sports injury surveillance are limited by lack of medical validation of self-reported injuries and/or incomplete information about injury consequences beyond time loss from sport. The aims of this study were to (a) evaluate the feasibility of the SMS, Phone, and medical Examination injury surveillance (SPEx) system (b) to evaluate the proportion of injuries and injury consequences reported by SPEx when compared to outcomes from a modified version of the Oslo Sports Trauma Research Centre (OSTRC) Overuse Injury Questionnaire. We followed 679 elite adolescent handball players over 31 weeks using the SPEx system. During the last 7 weeks, we also implemented a modified OSTRC questionnaire in a subgroup of 271 players via telephone interviews. The weekly response proportions to the primary SPEx questions ranged from 85% to 96% (mean 92%). SMS responses were received from 79% of the participants within 1 day. 95% of reported injuries were classified through the telephone interview within a week, and 67% were diagnosed by medical personnel. Comparisons between reported injuries from SPEx and OSTRC demonstrated fair (κ = 39.5% [25.1%-54.0%]) to substantial prevalence-adjusted bias-adjusted kappa (PABAK = 66.8% [95% CI 58.0%-75.6%]) agreement. The average injury severity score difference between SPEx and the OSTRC approach was -0.2 (95% CI -3.69-3.29) of possible 100 with 95% limits of agreement from(-14.81-14.41). These results support the feasibility and validity of the SPEx injury surveillance system in elite youth sport. Future studies should evaluate the external validity of SPEx system in different cohorts of athletes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".