An Analysis of Patient Presentations at a 2-Day Mass-participation Cycling Event: The Ride to Conquer Cancer Case Series, 2010-2012
Bibliographic record
Abstract
OBJECTIVE: To analyze the unique factors involved in providing medical support for a long-distance, cross-border, cycling event, and to describe patient presentations and event characteristics for the British Columbia (BC) Ride to Conquer Cancer from 2010 through 2012. METHODS: This study was a 3-year, descriptive case series report. Medical encounters were documented, prospectively, from 2010-2012 using an online registry. Data for event-related variables also were reported. RESULTS: Providing medical support for participants during the 2-day ride was complicated by communication challenges, weather conditions, and cross-border issues. The total number of participants for the ride increased from 2,252 in 2010 to 2,879 in 2011, and 3,011 in 2012. Patient presentation rates (PPRs) of 125.66, 155.26, and 198.93 (per 1,000 participants) were documented from 2010 through 2012. Over the course of three years, and not included in the PPR, an additional 3,840 encounters for "self-treatment" were documented. CONCLUSIONS: The Ride to Conquer Cancer Series has shown that medical coverage at multi-day, cross-national cycling events must be planned carefully to face a unique set of circumstances, including legislative issues, long-distance communication capabilities, and highly mobile participants. This combination of factors leads to potentially higher PPRs than have been reported for noncycling events. This study also illuminates the additional workload "self-treatment" visits place on the medical team.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".