Does Preoperative Positron Emission Tomography with Computed Tomography Predict Nodal Status in Endometrial Cancer? A Pilot Study
Bibliographic record
Abstract
Fewer than 20% of women with endometrial cancer have positive nodes, and an accurate noninvasive imaging modality to assess lymph node status would be helpful in selecting those who need lymphadenectomy. The objective of this pilot study was to evaluate positron emission tomography with computed tomography (pet-ct) in predicting nodal status before surgery for endometrial cancer. Twelve patients were enrolled at a single tertiary care centre. The sensitivity and specificity of preoperative pet-ct in predicting nodal status were 53.3% and 99.6% respectively. Using pet-ct, all metastatic nodes may not necessarily be detected, especially nodes with microscopic disease. The sensitivity of this imaging modality has to be improved before it can routinely be used in the preoperative evaluation of endometrial cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".