Longer Wait Times Increase Overall Mortality in Patients With Bladder Cancer
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: We used population level data to determine the impact of extended wait times on the survival of patients who underwent radical cystectomy for bladder cancer. MATERIALS AND METHODS: We identified 2,535 patients who underwent cystectomy for bladder cancer in Ontario, Canada between 1992 and 2004 using administrative databases. A Cox proportional hazards model accounting for patient, pathological and health services variables that could affect wait times was created to assess the impact of wait time on survival. The tumor stage specific impact of waiting for cystectomy was also assessed. Cox regression analysis that modeled wait time using cubic splines was used to determine a maximum wait time within which optimal care can be provided. RESULTS: Median wait time from transurethral bladder resection to cystectomy was 50 days. Unadjusted and adjusted analyses demonstrated that prolonged wait times were significantly associated with a lower overall survival rate. The relative hazard of death with increasing wait times appeared greater for low stage vs high stage cancers. The cubic splines regression analysis revealed that the risk of death began to increase after 40 days. CONCLUSIONS: Treatment delay between transurethral bladder tumor resection and radical cystectomy resulted in worse overall survival. The effect of wait time was greatest in lower stage lesions. The suggested maximum wait time from transurethral bladder tumor resection to cystectomy was 40 days. Further studies assessing disease-free survival are required to corroborate these findings.
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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) | 0.000 | 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 it