Clinical management of acute low back pain in elite and subelite rowers: a Delphi study of experienced and expert clinicians
Bibliographic record
Abstract
OBJECTIVES: Rowing-related low back pain (LBP) is common but published management research is lacking. This study aims to establish assessment and management behaviours and beliefs of experienced and expert clinicians when elite and subelite rowers present with an acute episode of LBP; second, to investigate how management differs for developing and masters rowers. This original research is intended to be used to develop rowing-related LBP management guidelines. METHODS: A three-round Delphi survey was used. Experienced clinicians participated in an internet-based survey (round 1), answering open-ended questions about assessment and management of rowing-related LBP. Statements were generated from the survey for expert clinicians to rate (round 2) and rerate (round 3). Consensus was gained when agreement reached a mean of 7 out of 10 and disagreement was 2 SD or less. RESULTS: Thirty-one experienced clinicians participated in round 1. Thirteen of 20 invited expert clinicians responded to round 2 (response rate 65%) and 12 of the 13 participated in round 3 (response rate 92%).One hundred and fifty-three of 215 statements (71%) relating to the management of LBP in elite and subelite rowers acquired consensus status. Four of six statements (67%) concerning developing rowers and two of four (50%) concerning masters rowers gained consensus. CONCLUSION: In the absence of established evidence, these consensus-derived statements are imperative to inform the development of guidelines for the assessment and management of rowing-related LBP. Findings broadly reflect adult LBP guidelines with specific differences. Future research is needed to strengthen specific recommendations and develop best practice guidelines in this athletic population.
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".