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Record W3142780458 · doi:10.1097/igc.0000000000001216

The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology Guidelines for the Management of Patients With Cervical Cancer

2018· article· en· W3142780458 on OpenAlexfundno aff
David Cibula, Richard Pötter, François Planchamp, Denis Querleu, Elisabeth Åvall‐Lundqvist, Christhardt Köhler, Sigurd Lax, Jacob Christian Lindegaard, Umesh Mahantshetty, Patrice Mathevet, W. Glenn McCluggage, Raj Naik, Remi A. Nout, Sandro Pignata, Jordi Ponce, Francesco Raspagliesi, Alexandros Rodolakis, Karl Tamussino, Pauline Wimberger, Maria Rosaria Raspollini

Bibliographic record

VenueInternational Journal of Gynecological Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsnot available
FundersInstitute of Cancer ResearchInstitut National Du CancerEuropean SocieTy for Radiotherapy and Oncology
KeywordsMedicineCervical cancerRadiation oncologyGynecologic oncologyClinical OncologyGynaecological cancerOncologyInternal medicineRadiation therapyGeneral surgeryGynecologyCancer

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.004

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 head0.066
GPT teacher head0.386
Teacher spread0.320 · 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 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

Citations479
Published2018
Admission routes1
Has abstractno

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