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Record W2945679037 · doi:10.1016/j.clon.2019.05.003

Generalisability of Common Oncology Clinical Trial Eligibility Criteria in the Real World

2019· article· en· W2945679037 on OpenAlexaff
Safiya Karim, Yuan Xu, Shiying Kong, Omar Abdel‐Rahman, May Lynn Quan, Winson Y. Cheung

Bibliographic record

VenueClinical Oncology · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineClinical OncologyClinical trialMedical physicsReal world evidenceOncologyInternal medicineIntensive care medicineCancer

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)0.605
metaresearch head score (Gemma)0.870
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 score0.395
Threshold uncertainty score0.487

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6050.870
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.018
Bibliometrics0.0070.007
Science and technology studies0.0020.010
Scholarly communication0.0100.006
Open science0.0070.007
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0190.002

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 head0.781
GPT teacher head0.670
Teacher spread0.110 · 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

Citations61
Published2019
Admission routes1
Has abstractno

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