God vård på lika villkor vid hjärtinfarkt i dagens Sverige. Geografiska skillnader i dödlighet utan betyd
Bibliographic record
Abstract
It is a known fact that the 1990s brought a decrease in mortality after myocardial infarction in Sweden but that differences in mortality rates following myocardial infarction still remain between the Swedish counties. Unresolved, however, are questions as to what these inter-county differences mean for the individual patient and what role hospital care plays in this context. We analysed all patients aged 64-85 years who were hospitalised following diagnosis of myocardial infarction in Sweden during the period 1993-1996. To gain an understanding of the relevance of geographical differences in mortality after myocardial infarction for the individual patient we applied multi-level regression analysis and calculated county and hospital median odds ratios (MORs) in relation to 28-day mortality. For hospitalised patients with myocardial infarction, being cared for in another hospital with higher mortality would increase the risk of dying by 9% (MOR=1.09) in men and 12% in women. If these patients moved to another county with higher mortality the risk would increase by 7% and 3%, respectively. The small geographical differences in 28-day mortality after myocardial infarction found in Sweden suggest a high degree of equality across the country; however, further improvement could be achieved in hospital care, especially for women - an issue that deserves further analysis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.020 |
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".