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Record W2054569014 · doi:10.1007/s10198-010-0261-3

Atrial fibrillation: the cost of illness in Sweden

2010· article· en· W2054569014 on OpenAlexaff
Lisa Ericson, Lennart Bergfeldt, Ingela Björholt

Bibliographic record

VenueThe European Journal of Health Economics · 2010
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsNordic Life Science Pipeline (Canada)
FundersSahlgrenska UniversitetssjukhusetSanofi
KeywordsMedicineHealth economicsIndirect costsAtrial fibrillationPublic healthEmergency medicineHealth carePsychological interventionPopulationAmbulatory carePopulation healthHealth administrationTotal costIntensive care medicineMedical emergencyPediatricsEnvironmental healthInternal medicineBusiness

Abstract

fetched live from OpenAlex

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 <euro>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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.344
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2010
Admission routes1
Has abstractyes

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