Improved reporting of overuse injuries and health problems in sport: an update of the Oslo Sport Trauma Research Center questionnaires
Bibliographic record
Abstract
In 2013, the Oslo Sports Trauma Research Center Overuse Injury Questionnaire (OSTRC-O) was developed to record the magnitude, symptoms and consequences of overuse injuries in sport. Shortly afterwards, a modified version of the OSTRC-O was developed to capture all types of injuries and illnesses-The Oslo Sports Trauma Research Center Questionnaire on Health Problems (OSTRC-H). Since then, users from a range of research and clinical environments have identified areas in which these questionnaires may be improved. Therefore, the structure and content of the questionnaires was reviewed by an international panel consisting of the original developers, other user groups and experts in sports epidemiology and applied statistical methodology. Following a review panel meeting in October 2017, several changes were made to the questionnaires, including minor wording alterations, changes to the content of one question and the addition of questionnaire logic. In this paper, we present the updated versions of the questionnaires (OSTRC-O2 and OSTRC-H2), assess the likely impact of the updates on future data collection and discuss practical issues related to application of the questionnaires. We believe this update will improve respondent adherence and improve the quality of collected data.
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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 | 0.051 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".