Impact of Wait Times on Survival for Women With Uterine Cancer
Bibliographic record
Abstract
PURPOSE: To determine whether wait time from histologic diagnosis of uterine cancer to time of definitive surgery by hysterectomy had an impact on all-cause survival. PATIENTS AND METHODS: Women in Ontario with a confirmed histopathologic diagnosis of uterine cancer between April 1, 2000, and March 31, 2009, followed by surgery were identified in the Ontario Cancer Registry. Survival was calculated by using the Kaplan-Meier method. Factors were evaluated for their prognostic effect on survival by using Cox proportional hazards regression. Wait time was evaluated in a multivariable model after adjusting for other significant factors. RESULTS: The final study population included 9,417 women; 51.9% had surgery by a gynecologist, and 69.9% had endometrioid adenocarcinoma. Five-year survival for women with wait times of 0.1 to 2, 2.1 to 6, 6.1 to 12, or more than 12 weeks was 71.1%, 81.8%, 79.5%, and 71.9%, respectively. Wait times of ≤ 2 weeks were adversely prognostic for survival after adjusting for other significant factors in the multivariable model, and patients with wait times of more than 12 weeks had worse survival than those who had wait times between 2.1 and 12.0 weeks. CONCLUSION: To the best of our knowledge, this is the first report in a large population-based cohort demonstrating that longer wait times from diagnosis of uterine cancer to definitive surgery have a negative impact on overall survival.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".