Illness and injury in athletes during the competition period at the London 2012 Paralympic Games: development and implementation of a web-based surveillance system (WEB-IISS) for team medical staff
Bibliographic record
Abstract
BACKGROUND: In this study we describe (1) the implementation of a novel web-based injury and illness surveillance system (WEB-IISS) for use by a team of physicians at multisport events and (2) the incidence and characteristics of injuries and illness in athletes during the London 2012 Paralympic Games. METHODS: Overall, 3565 athletes from 160 of the 164 participating countries were followed daily over a 14-day period, consisting of a precompetition period (3 days), and a competition period (11 days) (49 910 athlete-days). Daily injury and illness data were obtained from teams with their own medical support (78 teams, 3329 athletes) via the WEB-IISS, and without their own medical support through the London Organising Committee of the Olympic Games and Paralympic Games database (82 teams and 236 athletes). RESULTS: There were no differences between incidence rates (IR) of injury and illness, or between the precompetition and competition periods. The IR of injury during the competition period was 12.1/1000 athlete-days, with an incidence proportion (IP) of 11.6% (95% CI 11.0% to 13.3%). Upper limb injuries (35%), particularly of the shoulder (17%) were most common. The IR of illness during the competition period was 12.8/1000 athlete-days (95% CI 12.18 to 1421), with an IP of 10.2%. The IP was highest in the respiratory system (27.4%), skin (18.3%) and the gastrointestinal (14.5%) systems. CONCLUSIONS: During the competition period, the IR and IP of illness and injury at the Games were similar and comparable to the observed rates in other elite competitions. In Paralympic athletes, the IP of upper limb injuries is higher than that of lower limb injuries and non-respiratory illnesses are more common.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".