The epidemiology of injuries in powerlifting at the <scp>L</scp>ondon 2012 <scp>P</scp>aralympic <scp>G</scp>ames: An analysis of 1411 athlete‐days
Bibliographic record
Abstract
Sport injury epidemiology has received increased recognition as a field of sport medicine research that can improve the health and safety of athletes. Injuries among Paralympic powerlifters have not previously been systematically studied. The purpose of this prospective cohort study was to characterize injuries among Paralympic powerlifters. Athletes competing in the sport of powerlifting were followed over the 7-day competition period of the 2012 London Paralympic Games. The main outcome measurements were injury incidence rate (IR; number of injuries per 1000 athlete-days) and injury incidence proportion (IP; injuries per 100 athletes). A total of 38 injuries among 163 powerlifters were documented. The overall IR was 33.3 injuries/1000 athlete-days (95% CI 24.0-42.6) and the overall IP was 23.3 injuries per 100 athletes (95% CI 16.8-29.8). The majority of injuries were chronic overuse injuries (61%). The most commonly injured anatomical region was the shoulder/clavicle (32% of all injuries), followed by the chest (13%) and elbow (13%). The information obtained in this study opens the door for future study into the mechanisms and details of injuries into powerlifters with physical impairments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".