Avoidable Mortality in the United States and Canada, 1980–1996
Bibliographic record
Abstract
A number of measures are currently used to evaluate health care performance for populations. Most measures focus on components of economic efficiency, medical efficacy, social acceptability, and organizational structure.1–4 Because of a lack of relevant data, health care outcomes are infrequently evaluated. Also, the relationship between health outcomes and health care is often obscured by various genetic, social, and environmental factors that, in addition to health care, influence health outcomes.5 Available evaluations of health care outcomes are usually focused on hospital or physician performance, as opposed to population health or entire health care systems. In 1976, Rutstein et al. proposed “avoidable mortality” as a simple and practical population-based method of counting “untimely and unnecessary deaths” from diseases for which effective public health and medical interventions are available.6 An excess of such deaths could be viewed as a signal of possible shortcomings in the health care system that warranted further investigation. For a sentinel disease to be defined as avoidable, there must be identifiable, effective interventions and available health care providers. Use of the avoidable mortality measure became common in Europe following refinements in the original Rutstein et al. disease groups by Charlton in the disease groups by Charlton et al.7 and subsequently the European Community Concerted Action Project on Health Services and “avoidable mortality” (ECCAP).8 In this study we examined avoidable mortality in the United States and Canada from 1980 to 1996. We postulated that there may be differences in avoidable mortality between the 2 countries, and that, if differences existed, avoidable mortality might be a useful population-based outcome measure that would encourage further evaluation and improvement of health care systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".