FIGO Staging System for Endometrial Cancer: Added Benefits of MR Imaging
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".