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Record W4392632527 · doi:10.1136/ijgc-2024-esgo.19

738 Surgical approach, preoperative LEEP/cone biopsy and patterns of recurrence and death in low-risk cervical cancer – analysis of the international CCTG CX.5/SHAPE phase III trial

2024· article· en· W4392632527 on OpenAlexaff
Sven� Mahner, Fabian Trillsch, Janice S. Kwon, Sarah E. Ferguson, Paul Bessette, Gwénaël Ferron, Amandine Maulard, Cor DDe Kroon, Willemien JVan Driel, John Tidy, Karin Williamson, Frédéric Goffin, Stephan Polterauer, Brynhildur Eyjolsdottir, Jae‐Weon Kim, Patrick Maguire, Barbara Schmalfeldt, Dongsheng Tu, Lois E. Shepherd, Marie Plante

Bibliographic record

VenueInternational Journal of Gynecological Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsThe Society of Gynecologic Oncology of CanadaUniversité de SherbrookePrincess Margaret Cancer CentreQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgeryHysterectomyCervical cancerRadical surgeryCancerInternal medicine

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.365
Teacher spread0.326 · 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 designObservational
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

Citations3
Published2024
Admission routes1
Has abstractno

Explore more

Same venueInternational Journal of Gynecological CancerSame topicEndometrial and Cervical Cancer TreatmentsFrench-language works237,207