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Combination Polypharmacy for Cardiovascular Disease Prevention in Men: A Decision Analysis and Cost‐Effectiveness Model

2008· article· en· W1972666522 on OpenAlexaff
Jonathan Newman, William A. Grobman, Philip Greenland

Bibliographic record

VenuePreventive Cardiology · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsColumbia College
Fundersnot available
KeywordsMedicinePolypharmacyAtenololHydrochlorothiazideDyslipidemiaFixed-dose combinationCost effectivenessQuality-adjusted life yearInternal medicineIntensive care medicineDiseaseBlood pressure

Abstract

fetched live from OpenAlex

Pharmacotherapies to lower blood pressure and cholesterol are effective in the primary prevention of cardiovascular disease (CVD). The use of fixed-dose medication combinations has not been well studied. The authors created a Markov model to analyze the cost-effectiveness of 4 fixed-dose medications for primary CVD prevention in men. The initial decision node was to treat or not treat men older than 55 years, without CVD, hypertension, or dyslipidemia with a combination of simvastatin, captopril, hydrochlorothiazide, and atenolol. Probability, costs, and effectiveness were derived from the literature. The outcome variable was marginal cost per quality-adjusted life-year (QALY). Sensitivity analyses were performed. The average cost of treatment was $70,000 compared with $93,000 for non-treatment. Treatment resulted in 13.62 QALYs vs 12.96 QALYs without treatment. Primary prevention of CVD with fixed-dose medications dominated "no-treatment." The use of a fixed-dose polypharmacy approach to CVD prevention in men older than 55 years may be cost-effective.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.291
GPT teacher head0.445
Teacher spread0.154 · 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

Citations17
Published2008
Admission routes1
Has abstractyes

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