Paralympic Medical Services for the 2010 Paralympic Winter Games
Bibliographic record
Abstract
OBJECTIVE: To present the planning and medical encounters for the 2010 Paralympic Winter Games. DESIGN: Prospective medical encounter study. SETTING: 2010 Paralympic Winter Games. PARTICIPANTS: Athletes, coaches, officials, workforce, volunteers, and media. ASSESSMENT OF RISK FACTORS: Sport type: alpine, Nordic, and sledge hockey and curling. Participant type: athlete, workforce, and spectators. Terrain and speed. MAIN OUTCOME MEASURES: Medical encounters entered in database at competitive (alpine skiing, biathlon, cross-country skiing, sledge hockey, and curling) and noncompetitive (Whistler and Vancouver Polyclinics, presentation centers, opening and closing ceremonies, media center, Paralympic Family Hotel) venues. RESULTS: Forty-two nations participated with 1350 Paralympic athletes, coaches, and officials. There were 2590 accredited medical encounters (657 athletes, 25.4%; 682 International Federation/National Paralympic Committee officials, 26.3%; 57 IPC, 2.2%; 8 media, 0.3%; 1075 workforce, 41.5%; 111 others, 4.3%) and 127 spectator encounters for a total of 2717 encounters. During the preopening period medical services saw 201 accredited personnel. The busiest venues during the Paralympic Games were the Whistler (1633 encounters) and Vancouver (748 encounters) Polyclinics. Alpine, sledge hockey, and curling were the busiest competitive venues. The majority of medical encounters were musculoskeletal (44.6%, n = 1156). Medical services recorded 1657 therapy treatments, 977 pharmaceutical prescriptions dispensed, 204 dental treatments, 353 imaging examinations (more than 50% from alpine skiing), and 390 laboratory tests. There were 24 ambulance transfers with 7 inpatient hospitalizations for a total of 24 inpatient days and 4 outpatient visits. CONCLUSIONS: The mandate to have minimal impact on the health services of Vancouver and the Olympic Corridor while offering excellent medical services to the Games was accomplished. This data will be valuable to future organizing committees.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".