Diabetes as a Tracer Condition in International Benchmarking of Health Systems
Bibliographic record
Abstract
OBJECTIVE -To assess the performance of health systems using diabetes as a tracer condition. RESEARCH DESIGN AND METHODS-We generated a measure of "case-fatality" among young people with diabetes using the mortality-to-incidence ratio (M/I ratio) for 29 industrialized countries using published data on diabetes incidence and mortality.Standardized incidence rates for ages 0 -14 years were extracted from the World Health Organization Dia-Mond study for the period 1990 -1994; data on death from diabetes for ages 0 -39 years were obtained from the World Health Organization mortality database and converted into agestandardized death rates for the period 1994 -1998, using the European standard population.RESULTS -The M/I ratio varied Ͼ10-fold.These relative differences appear similar to those observed in cohort studies of mortality among young people with type 1 diabetes in five countries.A sensitivity analysis showed that using plausible assumptions about potential overestimation of diabetes as a cause of death and underestimation of incidence rates in the U.S. yields an M/I ratio that would still be twice as high as in the U.K. or Canada.CONCLUSIONS -The M/I ratio for diabetes provides a means of differentiating countries on quality of care for people with diabetes.It is solely an indicator of potential problems, a basis for stimulating more detailed assessments of whether such problems exist, and what can be done to address them.
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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 | 0.081 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".