Impact du niveau neurologique sur les douleurs d’épaule chez le sportif en fauteuil roulant : une revue de littérature systématique
Bibliographic record
Abstract
Introduction: Last epidemiological data about the Paralympic Games highlight that the primary risk for wheelchair athletes is shoulder pain and/or shoulder injury. Various studies identified many risk factor but this review will focus on the impact of the lack of trunk control. Objective: To evaluate the impact of trunk control on shoulder pain among wheelchair athletes. Methodology : A systematic review based on the PRISMA items using Pubmed, Cochrane and Embase as databases. Any type of study could be included, and all languages were accepted as first-line. Study quality was assessed using the Newcastle-Ottawa scale, and the recommendations were weighted using the GRADE system. Results: Six studies were analysed, but few results are statistically significant. One poor-quality article found a correlation between lack of active trunk control and increased risk of shoulder pain. Two satisfying studies found opposing significant results concerning influence on pain intensity. Two studies of good and very good quality articles found a link between a high level of spinal cord injury and lack of shoulder strength. Discussion: As the level of confidence in these results (GRADE rating) is very week, we cannot conclude on a link between level of trunk control and shoulder pain among wheelchair athletes. The main limit of this review is the lack of good quality studies, emphasizing the neeed for further research.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".