Incidence and risk factors for back pain in young floorball and basketball players: A Prospective study
Bibliographic record
Abstract
The aim of this study was to investigate the incidence of back pain in young basketball and floorball players under 21 years of age. The secondary aim was to examine risk factors especially for low back pain (LBP). Nine basketball and nine floorball teams (n = 396) participated in this prospective follow-up study (2011-2014). Young athletes (mean age 15.8 ± 1.9) performed physical tests and completed a questionnaire at baseline. The follow-up lasted 1-3 years per player. During the follow-up, back pain reported by the players was registered on a weekly basis and verified by a study physician. The exposure time (AE) on team practices and games was recorded by the coach. Altogether back pain was reported 61 times by 51 players. The incidence of back pain was 87 per 1000 athlete-years and 0.4 per 1000 hours of AE. Hamstrings, quadriceps and iliopsoas extensibility and general joint hypermobility were not associated with LBP. Furthermore, no association between LBP and leg extension strength or isometric hip abduction strength asymmetry was found in these young basketball and floorball players. In conclusion, back pain can lead to a considerable time-loss from training and competition among young basketball and floorball players and the pain tends to reoccur. Lower extremity muscle extensibility, general joint hypermobility or investigated lower extremity strength measures were not associated with the risk of LBP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".