A NALYSIS OF A M EDICAL T ENT AT THE T ORONTO C ARIBANA P ARADE
Bibliographic record
Abstract
OBJECTIVES: To review the experience with an on-site medical tent for a mass gathering and to analyze patient records in a manner to more appropriately allocate resources and identify possible delays in definitive care. METHODS: The logistics of providing an on-site medical tent is reviewed, followed by a retrospective chart review of 126 patients over a two-year period. Prior to the chart review, an injury classification was developed that categorized patients based on the necessity of transport to hospital. Data were also analyzed for times of peak patient flow, types of injuries, and needless delays in definitive care. RESULTS: An average of 63 patients (95% CI 44-77) were seen in the tent and 1.3 patients sought care per 10,000 spectators. Peak times were between 1600 and 2000 hours. The average number of patients each hour was 6.5 (95% CI 0-13). Severe, intermediate, and minor injuries accounted for 16%, 38%, and 46% of total injuries, respectively. Nine cases were found where the patients arrived and left the medical tent by ambulance. Four of these instances may have represented a needless delay in definitive care. The details of each of these cases are reviewed. CONCLUSIONS: The results indicate that on-site medical coverage, with appropriate supports, is indeed safe. The frame-work provided with regard to setup and analysis of work-load will help others in the planning of medical care for similar mass gatherings.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".