Bibliographic record
Abstract
We wholeheartedly agree with Clark and Shinoda-Tagawa that injuries are an important source of “avoidable mortality.” Injuries, along with tobacco-related causes of death, are arguably the most important source of deaths that could be avoided by the public health care system. Injury and lung cancer death rates, like death rates for most other avoidable causes, have been decreasing in both Canada and the United States, but the rates are lower in Canada (Figure 1 ▶). For injuries, not only is the Canadian death rate much lower than that of the United States, but—again like death rates due to most other avoidable diseases—it is also decreasing faster. There are many other causes of death that could be avoided by the health care system that we did not include in our study. FIGURE 1 —Rates of avoidable deaths from injuries and lung cancer in Canada and the United States, 1980–1996. Since the study objective was to examine avoidable mortality as a potential performance measure of national health care systems, we felt it was important to select an unbiased group of avoidable deaths to ensure that disease groups were not specifically chosen to favor one country over another. We chose the same disease groups as the European Community Concerted Action Project on Health Services and Avoidable Mortality because this was the most established and widely disseminated evaluation of avoidable mortality.3 An additional benefit of using the European standard population is the ready comparison to the European Community and 13 member countries. Future studies should include injuries and other disease groups based on the principles of “avoidable mortality”3,4 where there is reliable ascertainment of cause of deaths.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.104 | 0.037 |
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".