Mobile Response by Medical First Responders at a Music Festival
Bibliographic record
Abstract
Background: The Belgian Red Cross provides first aid at 50 events with an attendance of more than 10,000 people every year.Since 2006, every patient encounter gets logged in a database called MedTRIS.The MedTRIS database contains more than 150,000 patient encounters.Methods: The triage category of a patient is recorded upon entering the first aid post.Four categories are used: without treatment, first aid, medical condition and medical emergency.A "medical emergency" requires immediate attention of a physician, a "medical condition/case" can wait.Other patient's characteristics, such as type of injury and type of event, are also recorded.All recorded information was coded for analysis in SPSS©.Results: 162.611 patient encounters are recorded in the MedTRIS database.16.989 (10,5%) patients needed medical attention.1080 (0,8% of total patient encounters) of these patients presented as a medical emergency.In the "medical condition/case" triage category the most prevalent type of injury was of the miscellaneous kind.This category represents -among others-urological and gynecological problems, eye abrasions and patients with chronic conditions.It is worth noting that some of the patients in the miscellaneous category probably belong in one of the other, more specific categories.Other types of injuries such as skin lesions, traumas and intoxications were roughly equally represented.However, in the "medical emergency" category, intoxications were more than three times as common as other type of injuries.Conclusion: True medical emergencies remain infrequent.An on-site physician needs to be capable to treat a multitude of different conditions.However, it is important to note that a medical emergency often concerns an intoxicated patient.Therefore, extra training in this specific type of injury is advisable.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.021 |
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".