Evolution of out-of-hospital emergency cardiac care: Heart attack therapy for a retired president helped modernize American emergency medical services
Bibliographic record
Abstract
In the late 1960s, American emergency medical services (EMS) began to upgrade from mere Red Cross first aid to systems that now provide sophisticated advanced life support. This revolution in EMS stemmed from two pioneering Belfast reports in The Lancet that described how early out-of-hospital coronary care saved lives. Inspired, a handful of American physicians implemented avant-garde programs in the USA. One such physician, Richard Crampton of the University of Virginia, supported by the university and by Charlottesville–Albemarle Rescue Squad staffs, led an early effort to provide out-of-hospital drug treatment and defibrillation via a mobile coronary care unit (MCCU) ambulance. Half a dozen high-profile local cases, including successful treatment of retired President Lyndon B. Johnson, demonstrated MCCU efficacy to the Virginia and American public via local and national press coverage. The economic feasibility of the MCCU system was established. With two Virginia colleagues, Crampton successfully lobbied for a bill to permit trained nonphysicians to render out-of-hospital cardiac care with no on-site physician. This MCCU-augmented EMS system reduced coronary deaths in Charlottesville and Albemarle County, Virginia. It also stimulated nationwide progress in care by EMS systems that yielded countless lives saved in the succeeding half-century.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".