La mortalidad evitable y no evitable: distribución geográfica en áreas pequeñas de España (1990–2001)
Bibliographic record
Abstract
OBJECTIVE: Comparison of mortality amenable to medical intervention (avoidable mortality) in small geographical areas provides a useful tool to analyse quality of health care services. Currently there are no studies that analyse avoidable mortality by geographical distribution in small areas for the whole of Spain. The aim of this study is to describe the geographical distribution of avoidable and non-avoidable mortality in small areas in Spain by sex for the period 1990-2001. METHODS: The 2.218 small areas considered consisted of municipalities or aggregated municipalities in the entirety of the Spanish territory. Avoidable deaths were analysed for the period 1990-2001. Empirical Bayes model-based estimates of age-adjusted relative risk were displayed in small-area maps for each group of causes of death by sex. RESULTS: There is an heterogeneous geographical distribution of avoidable mortality for both sexes. Areas with greater mortality are located in the south and northwest of Spain. Especially for hypertension, cerebrovascular disease and ischaemic heart disease in men there is a clear aggregation of deaths in these areas. Geographical distribution of non avoidable mortality in both sexes is similar to that described for these three causes. CONCLUSIONS: Geographical study of avoidable mortality in small areas for the whole of Spain permits the identification of areas with elevated mortality. Further research is necessary to clarify those factors related to avoidable mortality distribution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".