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Record W3171162733 · doi:10.1016/j.ccell.2021.05.012

Using real-word data to evaluate the effects of broadening eligibility criteria in oncology trials

2021· letter· en· W3171162733 on OpenAlexafffund
Enrique Sanz‐García, Benjamin Haibe‐Kains, Lillian L. Siu

Bibliographic record

VenueCancer Cell · 2021
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsVector InstitutePrincess Margaret Cancer CentrePublic Health OntarioOntario Institute for Cancer ResearchUniversity of TorontoUniversity Health Network
FundersAstellas Pharma CanadaNovartisAbbVieMerckCelgeneBayerAstraZenecaPrincess Margaret Cancer FoundationAmgenPfizerBristol-Myers Squibb
KeywordsClinical trialWord (group theory)MedicineComputer scienceClinical PracticeReal world dataOncologyMachine learningIntensive care medicineMedical physicsInternal medicineData scienceFamily medicineMathematics

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)0.432
metaresearch head score (Gemma)0.834
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4320.834
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.006
Science and technology studies0.0020.005
Scholarly communication0.0080.009
Open science0.0040.003
Research integrity0.0180.013
Insufficient payload (model declined to judge)0.0090.003

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 head0.729
GPT teacher head0.587
Teacher spread0.143 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations6
Published2021
Admission routes2
Has abstractno

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