Urologist Referral Delay and Its Impact on Survival after Radical Cystectomy for Bladder Cancer
Bibliographic record
Abstract
BACKGROUND: Evidence shows that wait times before bladder cancer surgery have been increasing, and wait time can negatively affect survival. We aimed to determine if a long delay caused by an indirect referral before a first urologist visit affects the survival of patients undergoing radical cystectomy for bladder cancer. METHODS: We analyzed data from 1271 patients who underwent surgery for bladder cancer during the decade 2000-2009. The cohort was obtained by linking two administrative databases in the province of Quebec. Patients were considered to have been directly referred to a urologist if they had 5 or fewer visits with a general practitioner before their first urologist visit; otherwise, they were considered to have been indirectly referred. The effect on survival after surgery of a longer delay before a first urologist visit was assessed using Cox regression models. RESULTS: Median referral delay for the study population was 30 days (56 days for women, 23 days for men; p < 0.0001). Indirect referral was observed for 49% of women and 33% of men. Compared with patients who were directly referred, those who were indirectly referred after first symptoms of bladder cancer experienced poorer survival (hazard ratio: 1.29; 95% confidence interval: 1.10 to 1.52). Women who were indirectly referred had a significant 47% greater risk of death after radical cystectomy. Men who were indirectly referred also experienced decreased survival (adjusted hazard ratio: 1.25; 95% confidence interval: 1.03 to 1.51). CONCLUSIONS: Patients indirectly referred to a urologist had an increased risk of mortality after surgery. Compared with men, women had longer wait times and poorer 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".