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Record W2051431260 · doi:10.1586/erp.11.45

The German method for setting ceiling prices for drugs: in some cases less data are required

2011· article· en· W2051431260 on OpenAlexaff
Afschin Gandjour, Amiram Gafni

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCeiling (cloud)Health careDownstream (manufacturing)Quality (philosophy)GermanActuarial scienceRisk analysis (engineering)Measure (data warehouse)Ceiling effectOperations managementMedicineBusinessEconomicsComputer scienceEngineeringAlternative medicineData mining

Abstract

fetched live from OpenAlex

The Institute for Quality and Efficiency in Health Care in Germany makes recommendations for ceiling prices of drugs based on an evaluation of the relationship between costs and effects in each therapeutic area. The analysis requires, when applicable, calculation of savings from avoided clinical events and increased future health expenditures from prolonging life (i.e., downstream costs). This article suggests that because of the specific requirements of the Institute for Quality and Efficiency in Health Care, in some cases calculation of downstream costs is not necessary when clinical outcomes are used as a measure of effectiveness. In this article, we identify these conditions. If they hold, it will reduce data requirements, the costs and time required to conduct evaluations, and the uncertainty 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.031
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.006
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0160.004

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 head0.647
GPT teacher head0.677
Teacher spread0.030 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2011
Admission routes1
Has abstractyes

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