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Record W2084073221 · doi:10.1377/hlthaff.19.2.92

Are Pharmaceuticals Cost-Effective? A Review Of The Evidence

2000· review· en· W2084073221 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueHealth Affairs · 2000
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionPrologueValue (mathematics)MedicineHealth careHealth policyPublic healthPublic relationsPolitical scienceNursingLawComputer scienceHistory

Abstract

fetched live from OpenAlex

PROLOGUE: A study of prescription drugs' cost-effectiveness might seem like an arcane research subject, but in fact it lies at the heart of the current debate over the growing use (and rising cost) of prescription drugs. Advocates argue that drugs offer value for the money, substituting for more costly and invasive treatments and hospitalizations. This study offers a cogent framework in which to determine prescription drugs' value in making treatment decisions. It is based on cost-utility analysis, which measures benefit in terms of quality-adjusted life years gained by drug therapies. Although, as the authors note, the approach has some limitations, such a measure enables comparisons across diverse conditions and allows the measure of benefit to take into account the value different people place on their treatment options. Peter Neumann is an assistant professor of policy and decision sciences in the Department of Health Policy and Management, Harvard School of Public Health (HSPH). He is deputy director of the Program on the Evaluation of Medical Technology there. Eileen Sandberg is a doctoral candidate in health policy at Harvard. Chaim Bell is a practicing internist and a doctoral candidate at the University of Toronto in Ontario, Canada; at the time of this study he was a visiting fellow at the HSPH. Patricia Stone is an assistant professor in the University of Rochester's School of Nursing and Department of Community and Preventive Medicine. She was an HSPH postdoctoral fellow. Richard Chapman is also a doctoral candidate in health policy at Harvard; he has worked at the Harvard Center for Risk Analysis, helping to develop a comprehensive database for cost-utility analyses. ABSTRACT: The argument that prescription drugs are cost-effective has been made both by the pharmaceutical industry to support rising drug prices and expenditures, and by advocates of expanded drug coverage for elderly and low-income persons. A new database of 228 published cost-utility analyses sheds light on the issue. According to published data, some drugs do save money or are cost-effective, but the issue depends critically on the context in which the drug is used and the intervention with which it is compared. Cost-utility analyses funded by the drug industry tend to report more favorable results than do those funded by nonindustry sources. Cost-effectiveness analysis can help policymakers to determine whether drugs and other interventions offer value for money.

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.

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)0.033
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.692
GPT teacher head0.580
Teacher spread0.112 · 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