Lumbosacral injuries in elite Paralympic athletes with limb deficiency: a retrospective analysis of patient records
Bibliographic record
Abstract
BACKGROUND: Compared to injury data in able-bodied athletes, relatively little literature exists for Paralympic athletes. Injury data underpins the design and evaluation of injury prevention strategies in elite sport. The aim of this study was to investigate frequency, characteristics and management of lumbosacral injuries in elite athletes with limb deficiency. METHODS: A retrospective analysis of injuries in elite athletes with limb deficiency (2008 to 2017) was conducted using available data extracted from The English Institute of Sport (EIS) clinical records. Eligibility criteria: funded athletes, eligible for EIS physiotherapy support with full or partial limb deficiency. Data were analysed descriptively using frequencies. RESULTS: A total of 107 injuries from 32 athletes were included. Participants comprised 18 men (59%), from 9 sports, with mean age for index injuries of 27 years (range 18 to 38 years) and 15 with congenital limb deficiency (47%). Average number of index injuries for congenital and traumatic limb deficient groups were 13 and 19, respectively. Where injury onset was recorded (n=79), half of injuries occurred during training (40%, n=43). Arthrogenic structures accounted for 32.7% of injuries, myogenic 26.2%, with neurogenic, discogenic and osteogenic each <5%. The number of treatments delivered in each injury episode ranged from 1 to 43, with symptom resolution taking 2 to 439 days. CONCLUSION: Elite athletes with limb deficiency experience lumbosacral injuries predominantly involving muscles and joints. While consistency and accuracy of data recording limits definitive conclusions, findings highlight the importance of precision in recording injury data as part of surveillance to enable implementation of effective injury prevention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".