2 International guideline for the management of acute low back pain in rowers – a proposal for an athlete care pathway
Bibliographic record
Abstract
It is estimated that up to 84 percent of adults have low back pain (LBP) at some time in their lives, with the 12 month incidence reported as 3%–18% worldwide. In the sport of rowing, between 32%–53% of athletes will experience LBP within a given year while point prevalence in adolescent rowers may be as high as 65% and 53% in males and females respectively. The lumbar spine is the most frequently injured region, accounting for up to 53% of all reported injuries amongst rowers with an incidence between 1.5 and 3.7/1000 hour of rowing and associated training. For many individuals, episodes of back pain are self-limited. The effect on rowers’ sporting careers, life-long disability and chronic pain, however, is unknown. Anecdotally, it is the injury most likely to be career-ending for rowers. There is no common strategy to address LBP in rowers despite evidence that specific information on management is desired by the rowing population worldwide. Thus, it is proposed that a consensus statement outlining best practice in the management of rowing LBP is produced and disseminated within the international rowing and sports medicine communities. An evidence-based approach using recent advances in understanding of factors associated with injury onset (including exposure, biomechanics and other intrinsic and extrinsic risk factors) is leading to the creation of the new guideline using both expert knowledge and the current evidence. A detailed literature search was conducted, reviewing the incidence/prevalence, risk factors, diagnosis and management of LBP in rowers. Discussions are being held between the authors and key experts (clinicians and researchers) in the international community to assimilate information and highlight key issues. To date, key research findings for optimal prevention and management include: type and mode of ergometer use, optimal lumbopelvic rhythm (moving through hips) and optimal loading including adequate recovery. The creation of this new guideline for management of LBP in rowers is designed to aid readers through diagnosis, investigations and management of LBP with a longer version for clinicians; it aims to be both user friendly and informative. This presentation will outline the steps taken and findings of this novel approach to reach consensus on an Athlete Care Pathway for LBP in rowers.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".