Medical Care Before and During the Winter Paralympic Games in Turin 2006, Vancouver 2010 and Sochi 2014
Bibliographic record
Abstract
Medical care in disabled sports is crucial both as prophylaxis and as ongoing medical intervention. The aim of this paper was to present changes in the quality of medical care over the consecutive Paralympic Games (PG). The study encompassed 31 paralympians: Turin (11), Vancouver (12), and Sochi (8) competing in cross-country skiing, alpine skiing, biathlon and snowboarding. The first, questionnaire-based, part of the study was conducted in Poland before the PG. The athletes assessed the quality of care provided by physicians, physiologists, dieticians, and physiotherapists, as well as their cooperation with the massage therapist and the psychologist. The other part of the study concerned the athletes' health before leaving for the PG, as well as their diseases and injuries during the PG. The quality of medical care was poor before the 2006 PG, but satisfactory before the subsequent PG. Only few athletes made use of psychological support, assessing it as poor before the 2006 PG and satisfactory before the 2010 and 2014 PG. The athletes' health condition was good during all PG. The health status of cross-country skiers was confirmed by a medical fitness certificate before all PG, while that of alpine skiers only before the 2014 PG. There were no serious diseases; training injuries precluded two athletes from participation. The quality of medical care before the PG was poor, however, became satisfactory during the actual PG. The resulting ad hoc pattern deviates from the accepted standards in medical care in disabled sports.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".