MétaCan
Menu
Back to cohort
Record W2139145948 · doi:10.1148/rg.321115045

FIGO Staging System for Endometrial Cancer: Added Benefits of MR Imaging

2012· article· en· W2139145948 on OpenAlex
Peter Beddy, Ailbhe C. O’Neill, Adam Kenji Yamamoto, Helen Addley, Caroline Reinhold, Evis Sala

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRadiographics · 2012
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineMagnetic resonance imagingStage (stratigraphy)Endometrial cancerRadiologyLymph nodeMalignancyCervical cancerParametrialCancerCervical carcinomaPathologyInternal medicine

Abstract

fetched live from OpenAlex

Endometrial cancer is the most commonly diagnosed gynecologic malignancy in the United States. This pathologic condition is staged with the International Federation of Gynecology and Obstetrics (FIGO) system. The FIGO staging system recently underwent significant revision, which has important implications for radiologists. Key changes incorporated into the 2009 FIGO staging system include simplification of stage I disease and removal of cervical mucosal invasion as a distinct stage. Magnetic resonance (MR) imaging is essential for the preoperative staging of endometrial cancer because it can accurately depict the depth of myometrial invasion, which is the most important morphologic prognostic factor and correlates with tumor grade, presence of lymph node metastases, and overall patient survival. Diffusion-weighted MR imaging and dynamic contrast medium-enhanced MR imaging are useful adjuncts to standard morphologic imaging and may improve overall staging accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.413
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.307
Teacher spread0.271 · 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