Prehospital emergency medical services.
Bibliographic record
Abstract
OBJECTIVES: Our primary objectives were to estimate how frequently emergency medical technicians with defibrillation skills (EMT-Ds) are forced to deal with prehospital do-not-resuscitate (DNR) orders, to assess their comfort in doing so, and to describe the prehospital care provided to patients with DNR orders in a system without a prehospital DNR policy (i.e., where resuscitation is mandatory). METHODS: Using Dillman methodology, the authors developed a 13-item survey and mailed it to 382 of 764 EMT-Ds in the metropolitan Toronto area. Responses were evaluated using 5-point Likert scales, limited-option and open-ended questions. Narrative responses were categorized. Two authors independently categorized narrative responses from 20 surveys, and kappa values for agreement beyond chance were determined. RESULTS: Among 382 EMT-Ds surveyed, 236 (62%) responded, of whom 221 (94%) answered the questionnaire. Overall, 126 of 219 (58%) indicated that they were called to resuscitate patients with DNR orders "sometimes," "frequently," or "all the time." In such situations, 22 of 207 (11%) stated they would honour the DNR order and 55 of 207 (27%) would honour the order but appear to provide basic resuscitation, in order to adhere to mandatory resuscitation regulations. Willingness to honour a DNR order did not vary by years of emergency medical service. EMT-Ds cited concern for the family and the patient, fear of repercussions and conflict with personal ethics as key factors contributing to this ethical dilemma. If legally allowed to honour DNR orders, 212 of 221 (96%) respondents would be comfortable with a written order and 137 of 220 (62%) with a verbal order. CONCLUSIONS: Prehospital DNR orders are common, and a significant number of EMT-Ds disregard current regulations by honouring them. EMT-Ds would be more comfortable with written than verbal DNR orders. An ethical prehospital DNR policy should be developed and applied.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.177 | 0.032 |
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".