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Record W2762643573 · doi:10.1093/annonc/mdx385.001

Magnitude of clinical benefit of randomized controlled trials supporting US Food and Drug Administration approval of drugs for solid tumours

2017· article· en· W2762643573 on OpenAlexaff
C. Moltó Valiente, Eitan Amir, Arnoud J. Templeton, L. Del Carpio Huerta, Joseph C. Del Paggio, Agustí Barnadas, Christopher M. Booth, Ariadna Tibau

Bibliographic record

VenueAnnals of Oncology · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsQueen's UniversityPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineClinical trialClinical endpointRandomized controlled trialLogistic regressionFood and drug administrationInternal medicineAdjuvantOncologyPharmacology

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)0.173
metaresearch head score (Gemma)0.453
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1730.453
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.016
Bibliometrics0.0030.003
Science and technology studies0.0010.006
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0140.001

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 head0.604
GPT teacher head0.592
Teacher spread0.012 · 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.

Study designObservational
DomainMethods
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

Citations0
Published2017
Admission routes1
Has abstractno

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