Long Term Correlation between Cancer and Cardiovascular Deaths According to Various Trends in Slovak Districts 1994-2003
Bibliographic record
Abstract
The aim of the article was to point out recorded cardiovascular deaths did not copy the real number of cardiovascular deaths, but may also include other causes of deaths. We evaluated all death notifications reported to the Statistical Office of the Slovak republic during the 10-year period, that is 519,680 cases in total. We analysed the year of death, location of death and the cause of death. The causes of deaths were split into three groups: cancer deaths, cardiovascular deaths (CVD) and other deaths. We used the fuzzy c cluster analysis and the basic epidemiological and statistical methods for the evaluation. We uncovered some Slovak districts had long-term higher mortality (Lucenec, Rimavská Sobota, Roznava, Trebisov and Krupina), as well as the other districts having the long-term lower mortality (Bratislava, Kosice, almost all Zilina region, Poprad, Spisská Nová Ves and Dunajská Streda). The cancer and cardiovascular deaths significantly correlated in terms of the Slovak districts. Evaluating the mutual causes of death proportion we identified two groups of Slovak districts; the first group of districts showing higher CVD deaths had lower cancer and other deaths, the second having higher proportion of cancer and other deaths reached the lower CVD deaths. It seems deaths have the similar pattern throughout the whole country, and the numerical differences are probably given only by the quality and the quantity of the death certification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".