Injury epidemiology and preparedness in powerlifting at the Rio 2016 Paralympic Games: An analysis of 1410 athlete‐days
Bibliographic record
Abstract
Purpose To describe injury epidemiology in Para powerlifters during the Rio 2016 Paralympic Games. Methods This cohort study was a sub-analysis of the web-based injury and illness surveillance system survey (WEB-IISS) carried out by the International Paralympic Committee (IPC) Medical Committee. The WEB-IISS survey was completed daily by the Chief Medical Officers of each National Paralympic Committee (NPC). Main outcome measures were injury incidence rate (IR; number of injuries per 1000 athlete-days), injury incidence proportion (IP; number of injuries per 100 athletes), and injury incidence rate ratio (IRR; the ratio between the calculated IRs). After the competition, a survey assessed the available clinical resources of each NPC. Results A total of 180 athletes participated in the time period; injuries for 141 athletes with their own medical support were recorded during the 10-day period, accounting for 1410 athlete-competition days of exposure. Overall IR was 15.6/1000 athlete-days (95% CI; 9.61-21.59). Most injuries were from chronic overuse (63.6%). The most commonly injured anatomical region was the shoulder (45.5%; IR = 7.09). There were no significant differences in injury patterns between male and female powerlifters (IRR = 0.78 [95% CI; 0.36-1.69], P-value = .699). The oldest age group (35-75) had the highest injury incidence rate (IR = 21.8 [95% CI; 12.63-30.96]). There was no significant difference in IP among lighter compared with heavier athletes. Of 34 NPCs, the majority of federations (91.6%-95.8%) felt their powerlifters have access to sports medicine doctors or sports medicine-trained clinicians who could implement and/or direct injury prevention protocols. Conclusions The information obtained in this study supports the need for injury prevention protocol development in this high-risk Para sport.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".