Medical facilities and encounters at the 2010 vancouver winter olympic games
Bibliographic record
Abstract
Background Epidemiological data and logistical details are crucial pieces of information for organisers of medical care at mass sporting events. Objective To outline the medical facilities at the 2010 Winter Olympic Games and to report the epidemiology of medical encounters. Design Prospective descriptive study. Setting Nine competition venues, six non-competition venues and two Polyclinics (Vancouver and Whistler). Medical services were available at all venues. The Polyclinics housed an inter-disciplinary team of health-care professionals. Imaging modalities (including CT and MRI) were on-site at the Polyclinics. Participants 2567 athletes, 82 National Olympic Committees, an estimated 4000 Officials, 25 000 Workforce/Volunteers, 10 000 Performers and 10 000 Media. Medical Services included 1560 volunteers and 19 full-time staff. Assessment Medical encounters were entered into a Medical Encounter Database (ATOS Origin). Games-Time reporting period was February 4th to 28th, inclusive. Computers were located at each Venue. Main outcome measure Medical encounters. Results There were 9038 medical encounters (8198 accredited; 840 spectator). The busiest venues were the Whistler Polyclinic (n=3124) and Vancouver Polyclinic (n=2930). Of the competition venues, the busiest were Whistler Sliding Centre (bobsleigh/luge/skeleton), Canada Hockey Place and Pacific Coliseum (figure skating and short track speed skating). Venues with the greatest number of spectator medical encounters were Canada Hockey Place (n=186), B.C. Place Stadium (Opening/Closing Ceremonies) (n=128) and UBC Sports Centre (hockey) (n=90). Sports with the highest and lowest athlete medical encounters were Alpine Skiing and Nordic Combined, respectively. The majority of encounters were musculoskeletal (MSK) (n=29%). There were 131 ground ambulance transfers and seven air ambulance extractions/transfers resulting in 14 hospital admissions and 43 outpatient visits. Conclusion The majority of medical encounters were seen at the Polyclinics. Although most were MSK, there was a high demand for other disciplines such as Dental and Imaging. Multi-disciplinary Polyclinics equipped with primary and ancillary services are essential at future Games.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".