Variability in the Identification and Reporting of Overuse Injuries Among Sports Injury Surveillance Data Collectors
Bibliographic record
Abstract
PURPOSE: This study examined variability in identifying and reporting overuse injuries among Certified Athletic Trainers (ATs). METHODS: This cross-sectional study of ATs participating in the National Collegiate Athletic Association's Injury Surveillance Program, utilized a novel online-only survey, consisting of seven hypothetical clinical scenarios representing various clinical presentations including overuse and acute elements. Participants reported clinical opinions regarding the role overuse played in each scenario (major contributor, not a major contributor, not enough information) and probability (0-100%) of classifying each scenario as having an overuse injury mechanism, then completed open-ended questions addressing their decision-making process. RESULTS: 74 ATs (25%) completed the survey. Six of the seven scenarios generated discordance in responses among the participating ATs. Variability in AT decisions involved: the progression of injury, duration of symptoms, and activity at time of injury. CONCLUSION: Developing a formalized definition of overuse injury may improve consistency and standardize methods for identifying and reporting overuse injuries within injury research.
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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 | 0.084 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".