The impact of wait times on oncological outcome in high‐risk patients with endometrial cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of surgical wait times on outcome of patients with grade 3 endometrial cancer. METHODS: All consecutive patients surgically treated for grade 3 endometrial cancer between 2007 and 2015 were included. Patients were divided into two groups based on the time interval between endometrial biopsy and surgery: wait time from biopsy to surgery ≤12 weeks (84 days) vs more than 12 weeks. Survival analyses were conducted using log-rank tests and Cox proportional hazards models. RESULTS: A total of 136 patients with grade 3 endometrial cancer were followed for a median of 5.6 years. Fifty-one women (37.5%) waited more than 12 weeks for surgery. Prolonged surgical wait times were not associated with advanced stage at surgery, positive lymph nodes, increased lymphovascular space invasion, and tumor size (P = .8, P = 1.0, P = .2, P = .9, respectively). In multivariable analysis adjusted for clinical and pathological factors, wait times did not significantly affect disease-specific survival (adjusted hazard ratio [HR]: 1.2, 95% confidence interval [CI], 0.6-2.5, P = .6), overall survival (HR: 1.1, 95% CI, 0.6-2.1, P = .7), or progression-free survival (HR: 0.9, 95% CI, 0.5-1.7, P = .8). CONCLUSION: Prolonged surgical wait time for poorly differentiated endometrial cancer seemed to have a limited impact on clinical outcomes compared to biological factors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".