Descriptive Epidemiology of Paralympic Sports Injuries
Bibliographic record
Abstract
Paralympic sports have seen an exponential increase in participation since 16 patients took part in the first Stoke Mandeville Games on the opening day of the 1948 London Olympic Games. More than 4,000 athletes took part in the London 2012 Paralympic Games. Few sporting events have seen such rapid evolution. This rapid pace of change also has meant challenges for understanding the injury risks of participation, not only because of the variety of sports, impairment types, the evolution of adapted equipment but also because of the inclusion of additional impairment types and development of new sports over time. Early studies were limited in scope but patterns of injuries are slowly emerging within Winter and Summer Paralympic sports. The IPC's London 2012 study is the largest to date with a prospective cohort study involving 49,910 athlete-days. The results identified large differences across sports and highlighted the need for longitudinal sport specific studies rather than solely games-time studies. This will require collaboration with international sports federations to examine injury patterns and risk factors for injury in this population to appropriately inform injury prevention strategies. Further studies will also need to address the impact of sporting participation, injury, and future health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".