Diagnostic accuracy and economic impact of three work-up strategies identifying risk groups in endometrial cancer, fully incorporating sentinel lymph node algorithm.
Bibliographic record
Abstract
BACKGROUND: According to the European Society for Medical Oncology/ European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology (ESMO/ESGO/ESTRO) Consensus Conference, the role of preoperative risk groups (RGs) in endometrial cancer (EC) is to direct surgical nodal staging. We compared diagnostic accuracy and economic impact of three work-up strategies to identify RGs. METHODS: A retrospective multicentre study including patients with early-stage EC. The three different work-up strategies were as follows:-Mondovì Hospital: transvaginal ultrasonography, pelvic magnetic resonance imaging (MRI); frozen section examination of the uterus in case of imaging discordance. High-risk patients underwent abdominal computed tomography.-Gemelli Hospital: transvaginal ultrasonography, MRI, One-Step Nucleic Acid Amplification (OSNA) of sentinel lymph node (SLN); frozen section examination of the uterus in case of imaging discordance.-Negrar Hospital: positron emission tomography (PET), frozen section examination of the uterus and of SLN. For statistical purposes patients were assigned, preoperatively and postoperatively, to two groups: group A (high-risk) and group B (not high-risk). RESULTS: Three hundred eighty-five patients were included (93 Mondovì, 215 Gemelli, 77 Negrar). Endometrial biopsy errors led to 47.3% misclassifications. Test accuracy of Mondovì, Gemelli and Negrar strategies was 0.83 (95%CI 0.734-0.901), 0.95 (95%CI 0.909-0.975) and 0.94 (95%CI 0.866-0.985), respectively. Preoperative work-up mean cost per patient in group A was €514.5 at Mondovì, €868.5 at Gemelli, and €1212.8 at Negrar hospital (p-value < 0.001), while in group B was €378.8 at Mondovì, €941.2 at Gemelli, and €1848.4 at Negrar hospital (p-value < 0.001). CONCLUSIONS: In our study, work-up strategies with more relevant economic impact showed a better diagnostic accuracy. Upcoming guidelines should specify recommendations about the gold standard work-up strategy, including the role of SLN.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".