Atrial fibrillation: the cost of illness in Sweden
Bibliographic record
Abstract
AIM: To provide an estimate of the annual cost of atrial fibrillation (AF) in Sweden. METHODS: Prevalence-based cost analysis of AF in Sweden for 2007. Direct medical (hospitalizations, hospital outpatient care, primary health care, non-pharmacological interventions, pharmaceuticals, and anticoagulation monitoring) and non-medical (transportation associated with health care visits) costs of AF, direct costs of AF complications (stroke and heart failure), and indirect costs (production loss), were included. Data were based on Swedish registries, reports and databases, published literature, and an expert panel. RESULTS: There were 100,557 individuals with AF as primary or secondary diagnosis that were either hospitalized or treated in hospital outpatient care in 2007. The total cost of AF was estimated at 708 million. The major cost driver was the direct cost of complications (54%), followed by hospitalization due to AF including AF as secondary diagnosis (18%), and production loss (12%). CONCLUSION: This is a comprehensive, nation-based cost analysis of AF where relevant data were derived from national registries covering the entire Swedish population. The results showed that the annual cost of AF was high in comparison with other diseases, but likely to be underestimated as a conservative approach was applied in the 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".