Predictors of preoperative delays before radical cystectomy for bladder cancer in <scp>Q</scp> uebec, <scp>C</scp> anada: a population‐based study
Bibliographic record
Abstract
OBJECTIVES: To characterise and measure different components of preoperative delays experienced by patients with bladder cancer before radical cystectomy (RC) in the province of Quebec, Canada and to identify the predictors of long waiting times. METHODS: We conducted a retrospective cohort study using the data of patients who underwent RC for bladder cancer between 2000 and 2009 in Quebec. The cohort was obtained with the linkage of two provincial health databases: the Régie de l'assurance maladie du Québec database (data on medical services dispensed to Quebec residents), and the Fichier des évenements démographiques de l'Institut de la statistique du Québec database (demographic data on births and deaths). For the entire cohort, we determined several components of delay from first medical visit related to bladder cancer symptoms until RC. Predictors of long delays were analysed using logistic regression. RESULTS: We analysed a total of 2778 patients who met the inclusion criteria. The median urologist referral delay was 32 days. The median delays between first urologist visit and RC and between transurethral resection of bladder tumour (TURBT) to RC were 90 days and 46 days, respectively. The median overall delay was 116 days. All components of delay progressively increased from the decade of the 1990s to the decade of the 2000s. Male gender was a protective factor for several components of delay, which suggests that gender-related variations may exist in the course of care for bladder cancer (odds ratio 0.67, 95% CI 0.50-0.89 for overall delay). Patient age and gender were associated with delayed urologist referral, delayed time to TURBT, and long overall waiting time. Factors related to the health system were associated with long cystoscopy delays. CONCLUSION: Median preoperative delays among patients with bladder cancer have been increasing and remain unacceptably long. Patient's age, gender and type of hospital facility were associated with long waiting times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".