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Record W2560928882 · doi:10.1093/annonc/mdw662

Fifth Ovarian Cancer Consensus Conference of the Gynecologic Cancer InterGroup (GCIG): clinical trial design for rare ovarian tumours

2016· article· en· W2560928882 on OpenAlexfundno aff
Alexandra Léary, Michael Quinn, Keiichi Fujiwara, Robert L. Coleman, Elise C. Kohn, Toru Sugiyama, Rosalind Glasspool, Isabelle Ray‐Coquard, Nicoletta Colombo, Monica Bacon, Alain G. Zeimet, Anneke M. Westermann, Eva María Gómez-García, Diane Provencher, Stephen Welch, William Small, David Millan, Aikou Okamoto, Gavin Stuart, Kazunori Ochiai

Bibliographic record

VenueAnnals of Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsnot available
FundersNational Cancer InstituteEisai CanadaCancer Research UKF. Hoffmann-La RochePfizer
KeywordsMedicineOvarian cancerClinical trialOncologyGerm cell tumorsSerous fluidRandomized controlled trialInternal medicineConsensus conferenceGynecologyCancerChemotherapy

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)0.315
metaresearch head score (Gemma)0.311
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.315
Threshold uncertainty score0.845

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3150.311
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0060.002
Open science0.0040.004
Research integrity0.0120.015
Insufficient payload (model declined to judge)0.0070.003

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 head0.354
GPT teacher head0.476
Teacher spread0.121 · 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
Domainnot available
GenreMethods

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

Citations38
Published2016
Admission routes1
Has abstractno

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