Le Grand Départ 2019 - health care management during a major planned event in the heart of Brussels (Belgium)
Bibliographic record
Abstract
Introduction: Mass gathering events (MGE), can attract sufficient attendees to strain the planning and response resources of the host community, state, or nation, thereby delaying the response to emergencies. The organization of such a MGE can be even more problematic when the event continues across much of downtown (including hospitals) and makes some parts of the city inaccessible. The aim of this study was describing the health care management of the Grand Départ of the Tour de France, July 6-7th, 2019. On both days, the stages drew crowds of 300,000 attendees, adding a quarter of the regular number of inhabitants of Brussels (1,2 million) and closing parts of downtown Brussels. Method: Data were retrospectively collected from the in-event health services (coordinated by the University Hospital Brussels). Data regarding medical interventions, as well as data generated by the advanced medical posts (AMP) were recorded and handed to us after anonymization. For analysis, patients were divided into two groups: those seen by first-aid responders and paramedics (triage code green) and those seen and treated by health professionals (emergency nurses and physicians) (triage codes yellow or red). Results: During the event, three AMPs were established along the route of the stage as were six ambulances, three mobile medical crews (one emergency nurse and one physician), and seven mobile first aid teams. Over the two days, 84 patients were seen; 80 green codes (95,2%), 3 yellow (3,6%), and one red (1,2%) resulting in a patient presentation rate of 0.28/1,000. In total eight patients were transported to hospital for further diagnosis and treatment (ambulance transfer rate: 0.02/1,000). Conclusion: In-event health services for this event proved adequate according to the number of attendees and the severity of the patients. No hospital reported disruptions to their standard operational capacity.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".