Longer Wait Times Increase Overall Mortality in Patients With Bladder Cancer
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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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".