Accuracy of Preoperative Tumor Grade and Intraoperative Gross Examination of Myometrial Invasion in Patients with Endometrial Cancer
Bibliographic record
Abstract
The Danish Gynaecological Cancer Society has stated that pelvic lymphadenectomy is warranted for all women with endometrioid endometrial cancer who have stage Ic disease or a poorly differentiated tumor. It is difficult, however, to predict the presence of diseased regional lymph nodes preoperatively. This prospective study focused on whether preoperative tumor grade and intraoperative gross assessment of myometrial invasion are accurate indicators of the need for pelvic lymphadenectomy. Participants were 72 consecutive women who had surgery in the years 2004–2006 for what was thought to be FIGO stage I endometrioid endometrial cancer. Endometrial biopsies were reviewed preoperatively by 2 experienced pathologists. Preoperative grading of endometrial biopsies correctly predicted tumor grade (well, moderately, or poorly differentiated) in 96% of the 72 patients. Tumor grade was underestimated in 2 cases and overestimated in 1. Gross evaluation of myometrial invasion correctly distinguished between stage Ia, Ib, and Ic disease in 89% of patients. Invasion was overestimated by intraoperative inspection in 4 instances and underestimated in 4 others. A mistaken clinical decision was made in 8 patients, 11% of the total. Three unnecessary lymphadenectomies were carried out, and 5 primary operations omitted lymphadenectomy despite its being warranted. A prediction that lymphadenectomy was not needed was 94% sensitive and 76% specific. These findings mean that preoperative grading of stage I endometrioid endometrial cancer and gross inspection of the uterus during surgery can help to decide whether or not to do a pelvic lymphadenectomy. Nevertheless, more reliable methods of evaluation are needed. At present, all operations should be done by surgeons who can perform lymphadenectomy should it be needed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".