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Record W4408173499 · doi:10.1016/j.ijgc.2025.101764

Outcomes of low-risk endometrial cancer with isolated tumor cells in the sentinel lymph nodes: a prospective, multi-center, single-arm, observational study (ENDO-ITC study)

2025· article· en· W4408173499 on OpenAlexaff
Luigi Antonio De Vitis, Giorgio Bogani, Francesco Raspagliesi, Octavio Arencibia, Beatriz Navarro Santana, Francesco Multinu, Vanna Zanagnolo, Glauco Baiocchi, Louise De Brot, Francesco Fanfani, Ilaria Capasso, Sabrina Piedimonte, Lara deGuerké, Alessandro Buda, Jessica Mauro, Manuela Alessio, Federica Filipello, Mario Beiner, Yfat Kadan, Andrea Papadia, Giuseppe Vizzielli, Stefano Restaino, Tommaso Grassi, Fabio Landoni, Tommaso Bianchi, Christoph Grimm, Stephan Polterauer, Giulio Ricotta, Alejandra Martínez, Paul Buderath, Rainer Kimmig, Vito Chiàntera, Behrouz Zand, Ignacio Zapardiel, Alicia Hernández, Stephanie Gill, Allan Covens, Christian Dagher, Tommaso Meschini, Giuseppe Cucinella, Gabriella Schivardi, Tommaso Occhiali, A. Lembo, Emilia Palmieri, Maryam Shahi, Angela J. Fought, Michaela E. McGree, Vera J. Suman, Nadeem R. Abu‐Rustum, Pedro T. Ramirez, Andrea Mariani, Gretchen Glaser

Bibliographic record

VenueInternational Journal of Gynecological Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHôpital Maisonneuve-Rosemont
FundersNational Cancer Institute
KeywordsMedicineObservational studyEndometrial cancerMulticenter studyOncologyLymphProspective cohort studyInternal medicineGynecologyCancerPathologyRandomized controlled trial

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.372
Teacher spread0.303 · 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

Citations1
Published2025
Admission routes1
Has abstractno

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