Impact of Wait Times on Survival for Women With Uterine Cancer
Bibliographic record
Abstract
Women diagnosed with early-stage uterine cancer (without spread) have a 5-year survival rate of 96%; the rate drops to 16% if this cancer is diagnosed after the cancer has spread more distantly. It has been suggested that a timely hysterectomy after diagnosis of uterine cancer may improve survival, but there are no data to support this suggestion. The aim of this study was to investigate whether a longer wait time from diagnosis of uterine cancer to definitive surgery by hysterectomy is associated with poorer overall survival. Data were obtained from the Ontario Cancer Registry for all women who had a histopathologic diagnosis of uterine cancer followed by a hysterectomy between 2000 and 2009. Survival time was estimated with the Kaplan-Meier method, and the prognostic effect on survival of individual factors was evaluated using univariate Cox proportional hazards regression. Wait time was then added to the optimal multivariate model to evaluate whether it remained a significant variable after adjusting for all other factors. Nine thousand four hundred seventeen women were included in the final study population; 51.9% underwent surgery by a gynecologist, and 69.9% of the tumors were endometrioid adenocarcinoma. The 5-year overall survival rates for patients with wait times of 0.1 to 2, 2.1 to 6, 6.1 to 12, and more than 12 weeks were 71.1%, 81.8%, 79.5%, and 71.9%, respectively. Multivariate analysis showed that wait time was an independent prognostic variable for overall survival (P < 0.001) after adjusting for other specified variables. Wait times of 2 weeks or less were adversely prognostic for survival in the multivariate model, and women with wait times of more than 12 weeks had worse survival than those with wait times of 2.1 to 12.0 weeks. This study provides the first data from a large population-based cohort that demonstrates that delay from a diagnosis of uterine cancer to definitive surgery has 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".