Bibliographic record
Abstract
When a patient is in critical condition and the nearest hospital is on diversion, what happens? Gary Vilke, MD, the medical director of the San Diego County Emergency Medical Services and an associate professor of emergency medicine at the University of California at San Diego, said, simply ignore the diversion. “If you are on acute status, you go the to the closest hospital. Period.” Peter Vicellio, MD, the vice chairman of emergency medicine at State University of New York at Stonybrook, noted that the anecdotes about people dying in transit while hospitals are on diversion are frequent. Diversion, he said, puts a human being in jeopardy. In an article in the Canadian Medical Association Journal, attorney Anne F. Walker, DVM, noted the case of 18-year-old Joshua Fleuelling, an asthmatic in severe respiratory distress, who was receiving cardiopulmonary resuscitation and did not have a pulse when his ambulance crew was told the nearest emergency department was on diversion. During the trip to an emergency department farther away, the ambulance crew was unable to defibrillate his heart and continued CPR. He arrived at the hospital with irreversible brain damage. Two days later, he was declared dead. After the incident, Dr. Walker said, the Toronto ambulance dispatch center told its personnel to transport critically ill patients to the nearest hospital, regardless of its diversion status. She also noted that medical associations in Canada and the United States have maintained that it is the ethical duty of physician to provide patients with emergency care (CMAJ 2002;166[4]:465). “Basically, there is no case law to give guidance,” said Michael Schull, MD, an assistant professor of emergency medicine at the University of Toronto. “There is clearly a dilemma because a physician has a duty to patients on stretchers as well. If you are overcrowded and can't cope with those you have, is it ethical to accept more? Is it ethical to leave a patient in an ambulance another 10 minutes? What seems to be clear is that, by far, the majority of liability lies with the hospital rather than the physicians.”
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".