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Record W4394844963 · doi:10.1016/j.jval.2024.04.007

The Inflation Reduction Act: An Opportunity to Accelerate Confidence in Real-World Evidence in the United States

2024· editorial· en· W4394844963 on OpenAlexaff
Paul Arora, Alind Gupta, Emma Mackay, Bart Heeg, Kristian Thorlund

Bibliographic record

VenueValue in Health · 2024
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsHealth CanadaMcMaster UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsNegotiationContext (archaeology)Health careEmulationInflation (cosmology)Risk analysis (engineering)BusinessPublic economicsEconomicsPolitical scienceEconomic growthLaw

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.036
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.048
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.145
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0090.006
Bibliometrics0.0090.006
Science and technology studies0.0060.007
Scholarly communication0.0190.011
Open science0.0080.004
Research integrity0.0480.053
Insufficient payload (model declined to judge)0.0150.010

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 head0.593
GPT teacher head0.499
Teacher spread0.094 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations4
Published2024
Admission routes1
Has abstractno

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