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Cost‐effectiveness of clopidogrel in acute coronary syndromes in Sweden: a long‐term model based on the cure trial

2004· article· en· W2036731085 on OpenAlexaff
Peter Lindgren, Bengt Jönsson, Salim Yusuf

Bibliographic record

VenueJournal of Internal Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineClopidogrelCost effectivenessUnstable anginaAnginaQuality-adjusted life yearAcute coronary syndromeClinical trialEmergency medicineIntensive care medicineInternal medicineCardiologyAspirinMyocardial infarction

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this study was to evaluate the long-term cost-effectiveness of clopidogrel on top of standard therapy (including ASA) in patients with acute coronary syndromes without ST-segment elevation in Sweden. METHODS AND RESULTS: Incremental cost-effectiveness ratios (ICER) were assessed using a Markov model with transition probabilities estimated from the Swedish hospital discharge and cause of death registers. Patients were assumed to be treated for 1 year, with treatment effects (RR = 0.8) and costs taken from the Clopidogrel in Unstable Angina to prevent Recurrent ischaemic Events Trial. Two scenarios were analysed: with patients similar to those in the trial and with patients similar to those from the register. In the first scenario, the predicted net direct cost was 160 euro and the net total cost -54 euro, which with an incremental survival of 0.12 years give the ICER of 1365 euro per life-year gained from the health care payer perspective (including direct costs) and cost savings from the societal perspective (also including indirect costs). The net costs in the second scenario were 149 euro, giving an ICER of 1009 euro for both perspectives. CONCLUSIONS: Adding clopidogrel to standard therapy including ASA is cost-effective in the studied setting and compares favourably with other cardiovascular treatment and prevention strategies.

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)0.002
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: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.338
Teacher spread0.297 · 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 designRandomized trial
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

Citations49
Published2004
Admission routes1
Has abstractyes

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