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Record W3176933341 · doi:10.1016/s1470-2045(21)00077-2

The International Collaboration for Research methods Development in Oncology (CReDO) workshops: shaping the future of global oncology research

2021· review· en· W3176933341 on OpenAlexaff
Priya Ranganathan, Girish Chinnaswamy, Manju Sengar, Durga Gadgil, Shivakumar Thiagarajan, Balram Bhargava, Christopher M. Booth, Marc Buyse, Sanjiv Chopra, Chris Frampton, Satish Gopal, Nick Grant, Mark Krailo, Ruth E. Langley, Prashant Mathur, Xavier Paolettí, Mahesh Parmar, Arnie Purushotham, Douglas G. Pyle, Preetha Rajaraman, Martin R. Stockler, Richard Sullivan, Soumya Swaminathan, Ian F. Tannock, Edward L. Trimble, Rajendra Badwe, C.S. Pramesh

Bibliographic record

VenueThe Lancet Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreQueen's University
FundersMedical Research CouncilNational Institutes of HealthCancer Research UKWorld Health Organization
KeywordsContext (archaeology)OncologyMedicineInternal medicineObservational studyDeveloping countryCapacity buildingClinical trialGlobal healthClinical researchMedical educationPolitical sciencePublic healthNursingEconomic growth

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)0.134
metaresearch head score (Gemma)0.107
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.866
Threshold uncertainty score0.707

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1340.107
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.005
Science and technology studies0.0010.005
Scholarly communication0.0070.009
Open science0.0040.010
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0080.003

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 head0.464
GPT teacher head0.690
Teacher spread0.226 · 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 designNot applicable
DomainMethods
GenreReview

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

Citations42
Published2021
Admission routes1
Has abstractno

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