A prospective cohort study identifying risk factors for shoulder injuries in adolescent elite handball players: the Karolinska Handball Study (KHAST) study protocol
Bibliographic record
Abstract
BACKGROUND: Handball is a physical contact sport that includes frequent overhead throwing, and this combination leads to a high rate of shoulder injuries. Several factors have been associated with shoulder injuries in overhead athletes, but strong scientific evidence is lacking for most suggested risk factors. We therefore designed the Karolinska Handball Study (KHAST) with the aim to identify risk factors for shoulder injuries in adolescent male and female elite handball players studying at handball-profiled secondary schools in Sweden. Secondary objectives are to investigate whether shoulder function changes during the competition season and whether the physical profile of the players changes during their time in secondary school. METHODS: Players aged 15 to 19 years were included during the pre-season period of the 2014-2015 and the 2015-2016 seasons. At inclusion, players signed informed consent and filled in a questionnaire regarding playing position, playing level, previous handball experience, history of shoulder problems and athletic identity. Players also completed a detailed test battery at baseline evaluating the shoulder, neck and trunk. Players were then prospectively monitored weekly during the 2014-2015 and/or 2015-2016 competitive seasons regarding injuries and training/match workload. Results from the annual routine physical tests in the secondary school curriculum including bench press, deep squat, hand grip strength, clean lifts, squat jumps, counter movement jumps, <30 m sprints, chins, dips and Cooper's test will be collected until the end of the competitive season 2017-2018. The primary outcome is the incidence of shoulder injuries and shoulder problems. The secondary outcome is the prevalence of shoulder injuries and shoulder problems. DISCUSSION: Shoulder problems are frequent among handball players and a reduction of these injuries is therefore warranted. However, in order to introduce appropriate preventive measures, a detailed understanding of the underlying risk factors is needed. Our study has a high potential to identify important risk factors for shoulder injuries in adolescent elite handball players owing to a large study sample, a high response rate, data collection during consecutive seasons, and recording of potential confounding factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".