Helping Runners Under Extreme Heat: The 2017 Montreal Half-Marathon Experience
Bibliographic record
Abstract
Introduction: The 2017 Montreal Half-Marathon was held on September 24th despite a record-breaking, out-of-season heatwave. The Urgences-santé Corporation (USC), Quebec’s largest emergency medical service (EMS), was tasked with coordinating and delivering prehospital response for over 15,000 runners at a time when the province’s paramedics were on strike. Aim: USC’s mission was to ensure runner safety under extreme conditions with limited staffing. In conjunction with the event’s medical teams, we implemented a new approach that oriented patients to the event’s clinic with the aim of limiting ambulance transports off-site and thus optimizing resources by promoting a “treat and release” principle. Methods: Emergency response was organized around the event’s clinic, which offered a level of care comparable to proximate emergency departments, including mass-cooling capacities. This capacity allowed us to modify provincial protocols, and thus prioritize treating patients on-site instead of transporting them to a hospital. Consequently, the prehospital response on the course could be assured with only 15 ambulances (staffed by managers) and a single team deployed at the event’s clinic, acting as transport officers. Heatstroke identification protocols were reinforced for the safety of the runners and spectators. Results: A total of 1,071 participants received medical attention, including 24 who were treated for a heat-related incident. On the course, 32 were evaluated by paramedics and 20 were transported to the event’s clinic. Only 7 patients were transferred from the clinic to a hospital, of which only one was for a heat-related incident. No deaths resulted from the race. Discussion: By anticipating and preparing for the extreme heat, the coordinated prehospital response safely reduced off-site transports, minimizing treatment delays for patients, and maximizing the use of on-site resources. We attribute this success to a strong collaboration with the race organizers, the presence of an on-site clinic, and an increase in prehospital resources.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".