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Record W1911616118 · doi:10.1377/hlthaff.2014.0552

Comparative Effectiveness And Cost-Effectiveness Analyses Frequently Agree On Value

2015· article· en· W1911616118 on OpenAlexaff
Henry A. Glick, Sean McElligott, Mark V. Pauly, Richard J. Willke, Henry Bergquist, Jalpa A. Doshi, Lee A. Fleisher, Bruce Kinosian, Eleanor M. Perfetto, Daniel Polsky, J. Sanford Schwartz

Bibliographic record

VenueHealth Affairs · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsCost effectivenessComparative effectiveness researchOutcomes researchCost–benefit analysisQuality-adjusted life yearIncremental cost-effectiveness ratioMedicineActuarial scienceValue (mathematics)Health careEconomicsRisk analysis (engineering)Computer scienceAlternative medicine

Abstract

fetched live from OpenAlex

The Patient-Centered Outcomes Research Institute, known as PCORI, was established by Congress as part of the Affordable Care Act (ACA) to promote evidence-based treatment. Provisions of the ACA prohibit the use of a cost-effectiveness analysis threshold and quality-adjusted life-years (QALYs) in PCORI comparative effectiveness studies, which has been understood as a prohibition on support for PCORI's conducting conventional cost-effectiveness analyses. This constraint complicates evidence-based choices where incremental improvements in outcomes are achieved at increased costs of care. How frequently this limitation inhibits efficient cost containment, also a goal of the ACA, depends on how often more effective treatment is not cost-effective relative to less effective treatment. We examined the largest database of studies of comparisons of effectiveness and cost-effectiveness to see how often there is disagreement between the more effective treatment and the cost-effective treatment, for various thresholds that may define good value. We found that under the benchmark assumption, disagreement between the two types of analyses occurs in 19 percent of cases. Disagreement is more likely to occur if a treatment intervention is musculoskeletal and less likely to occur if it is surgical or involves secondary prevention, or if the study was funded by a pharmaceutical company.

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)0.023
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.676
GPT teacher head0.544
Teacher spread0.132 · 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 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

Citations22
Published2015
Admission routes1
Has abstractyes

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