MétaCan
Menu
← Back to cohort
Record W4226059009 · doi:10.2139/ssrn.4082917

Survival Prognostic and Surrogate Values of the Early Modeled Ca-125 Kelim Score in First-Line Treatment of Ovarian Cancer: Results from The Gcig Individual Patient Data Meta-Analysis

2022· article· en· W4226059009 on OpenAlexaff
Pauline Corbaux, Benoît You, Rosalind Glasspool, Nozomu Yanaihara, Anna V. Tinker, Kristina Lindemann, I. Ray-Coquard, Mansoor Raza Mirza, Fabien Subtil, Olivier Colomban, Julien Péron, Eleni Karamouza, Iain A. McNeish, Samantha Hinsley, Tatsuo Kagimura, Stephen Welch, Liz-Anne Lewsley, Xavier Paolettí, Adrian Cook

Bibliographic record

VenueSSRN Electronic Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMeta-analysisOvarian cancerOncologyInternal medicineMedicineSurrogate endpointFirst linePatient dataCancerComputer science

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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.032
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.300
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
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
Published2022
Admission routes1
Has abstractno

Explore more

Same venueSSRN Electronic Journal→Same topicOvarian cancer diagnosis and treatment→French-language works237,207→