Low back pain status in elite and semi-elite Australian football codes: a cross-sectional survey of football (soccer), Australian rules, rugby league, rugby union and non-athletic controls
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Our understanding of the effects of football code participation on low back pain (LBP) is limited. It is unclear whether LBP is more prevalent in athletic populations or differs between levels of competition. Thus it was the aim of this study to document and compare the prevalence, intensity, quality and frequency of LBP between elite and semi-elite male Australian football code participants and a non-athletic group. METHODS: A cross-sectional survey of elite and semi-elite male Australian football code participants and a non-athletic group was performed. Participants completed a self-reported questionnaire incorporating the Quadruple Visual Analogue Scale (QVAS) and McGill Pain Questionnaire (short form) (MPQ-SF), along with additional questions adapted from an Australian epidemiological study. Respondents were 271 elite players (mean age 23.3, range 17-39), 360 semi-elite players (mean age 23.8, range 16-46) and 148 non-athletic controls (mean age 23.9, range 18-39). RESULTS: Groups were matched for age (p = 0.42) and experienced the same age of first onset LBP (p = 0.40). A significant linear increase in LBP from the non-athletic group, to the semi-elite and elite groups for the QVAS and the MPQ-SF was evident (p < 0.001). Elite subjects were more likely to experience more frequent (daily or weekly OR 1.77, 95% CI 1.29-2.42) and severe LBP (discomforting and greater OR 1.75, 95% CI 1.29-2.38). CONCLUSION: Foolers in Australia have significantly more severe and frequent LBP than a non-athletic group and this escalates with level of competition.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".