Validity of the SMS, Phone, and medical staff Examination sports injury surveillance system for time‐loss and medical attention injuries in sports
Bibliographic record
Abstract
The accurate measurement of sport exposure time and injury occurrence is key to effective injury prevention and management. Current measures are limited by their inability to identify all types of sport-related injury, narrow scope of injury information, or lack the perspective of the injured athlete. The aims of the study were to evaluate the proportion of injuries and the agreement between sport exposures reported by the SMS messaging and follow-up telephone part of the SMS, Phone, and medical staff Examination (SPEx) sports injury surveillance system when compared to measures obtained by trained on-field observers and medical staff (comparison method). We followed 24 elite adolescent handball players over 12 consecutive weeks. Eighty-six injury registrations were obtained by the SPEx and comparison methods. Of them, 35 injury registrations (41%) were captured by SPEx only, 10 injury registrations (12%) by the comparison method only, and 41 injury registrations (48%) by both methods. Weekly exposure time differences (95% limits of agreement) between SPEx and the comparison method ranged from -4.2 to 6.3 hours (training) and -1.5 to 1.0 hours (match) with systematic differences being 1.1 hours (95% CI 0.7 to 1.4) and -0.2 (95% CI -0.3 to -0.2), respectively. These results support the ability of the SPEx system to measure training and match exposures and injury occurrence among young athletes. High weekly response proportions (mean 83%) indicate that SMS messaging can be used for player measures of injury consequences beyond time-loss from sport. However, this needs to be further evaluated in large-scale studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".