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Empirical Evidence on the Value of Pharmaceuticals

2012· book-chapter· en· W253643620 on OpenAlexaff
Craig Garthwaite, Mark Duggan

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsHealth benefitsPublic economicsValue (mathematics)Medical prescriptionCost–benefit analysisEmpirical evidenceHealth economicsEconomicsBusinessActuarial scienceMedicineHealth carePharmacologyEconomic growthPolitical scienceTraditional medicine

Abstract

fetched live from OpenAlex

Abstract This article begins by summarizing the existing evidence concerning the effect of pharmaceuticals on overall health. It then examines evidence of the health benefits of pharmaceuticals for the most commonly used treatments for widespread chronic and life-threatening conditions. It focuses on the most widespread conditions and those for which the utilization of prescription medication has changed the most dramatically over the last two decades. A broader question about the total value of pharmaceuticals involves the net benefit of these medications. There is a growing debate in the literature specifically about whether new drugs are worth more than their costs. The largest debate focuses on whether spending on these new drugs leads to even larger decreases in nonprescription drug spending whether the new drugs are cost-effective (i.e., providing enough health benefits to outweigh their costs relative to an alternative treatment method), or neither. The article considers existing evidence on the net benefits of these medications in terms of cost savings from nondrug health spending. Finally, it discusses the growing body of literature focusing on the nonhealth benefits of pharmaceuticals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.271
GPT teacher head0.314
Teacher spread0.043 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations8
Published2012
Admission routes1
Has abstractyes

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