The Effect of Injury Definition and Surveillance Methodology on Measures of Injury Occurrence and Burden in Elite Volleyball
Bibliographic record
Abstract
A time-loss injury definition continues to be the most widely used injury definition despite evidence that it fails to accurately capture overuse injuries. An overuse injury questionnaire, using an "all complaints" definition has been created to address the limitation of a time-loss definition. The main aim of this work was to determine the effect that injury definition and registration methodology has on the collection of knee injuries among elite level volleyball players. To reach this goal, seventy-two volleyball players were prospectively followed over 32-weeks. Time-loss injuries were captured using an individual injury report form (IIRF). Study participants completed an overuse injury questionnaire (mOIQ) via a weekly short message service (SMS). The IIRF captured 15 time-loss knee injuries in 72 study participants (20%). Based on the mOIQ, 84.7% of participants reported having a knee problem and 66.7% sustained a substantial knee problem. All IIRF knee injuries captured were also registered by the mOIQ. Agreement on the specific diagnosis occurred for 66.7% of injuries resulting in a moderate Kappa score of 0.51. In conclusion, an overuse injury questionnaire provided a greater understanding of the magnitude and burden of knee injuries in this population.
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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 | 0.199 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".