Use of Treat-and-Release Medical Directives for Paramedics at a Mass Gathering
Bibliographic record
Abstract
INTRODUCTION: Paramedics provide a substantial proportion of care at mass gatherings but do not typically release patients without physician assessment. OBJECTIVE: To evaluate treat-and-release medical directives implemented at a large single-day summer rock concert. METHODS: Medical directives allowed paramedics to administer acetaminophen, dimenhydrinate, diphenhydramine, or polymyxin B ointment for common complaints without evidence of serious illness on history or examination. After treatment, patients were released or transferred to a medical facility according to predefined criteria. Patient demographics, chief complaint, treatment, and disposition were obtained from paramedic records. To determine whether any patients released by paramedic subsequently required ambulance transport, all ambulance records were searched for a period of eight hours before to 24 hours after the event. RESULTS: More than 450,000 people attended the concert, with 1,870 presenting for medical attention. Four hundred seven patients received medications under the directives. No disposition was recorded in 13 cases. Two hundred ninety-nine patients were treated with acetaminophen, of whom 269 (90.0%) were released and 23 (7.7%) required additional care. Sixty-two patients received dimenhydrinate, 44 (71%) were released, and 14 (23%) required transport. Thirty-six patients received diphenhydramine, and 34 (94%) were released. Ten patients received polymyxin B and were released. No patient released by paramedics was found to have later required ambulance transport. CONCLUSIONS: Treat-and-release medical directives for paramedics at mass gatherings may help divert patients from requiring care at a medical facility. Future research is needed to determine the safety (morbidity and mortality) of these directives.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".