Patterns of referral for peri-operative chemotherapy among patients with muscle-invasive bladder cancer (MIBC): A population-based study.
Bibliographic record
Abstract
e15507 Background: Reasons for low uptake of peri-operative chemotherapy for MIBC are not well described. Here we report referral patterns from urology to medical oncology (MO) in routine clinical practice and subsequent use of chemotherapy. Methods: Treatment and physician billing records were linked to the population-based Ontario Cancer Registry to describe referral patterns from urology to MO and subsequent use of neoadjuvant or adjuvant chemotherapy (NACT/ACT) among all patients with MIBC treated with cystectomy in Ontario 1994-2008. Referral and treatment patterns were described over 3 study periods: 1994-1999, 2000-2004, 2005-2009. Logistic regression was used to analyze factors associated with referral to MO and use of NACT/ACT. Results: Eighteen percent (520/2944) of the study population was seen by MO prior to cystectomy and 25% (128/520) of referred cases were treated with NACT. The proportion of cases seen by MO before cystectomy decreased across the study periods (20%, 18%, 16%, p=0.075) and varied substantially across geographic regions (range 5-40%, p<0.001). Use of NACT among those patients seen by MO did increase over time (23%, 14%, 35%, p<0.001). Among patients not treated with neoadjuvant chemotherapy or radiation, 39% (1085/2809) were seen by MO following cystectomy; 51% (548/1085) of referred patients were treated with ACT. In multivariate analysis, referral to MO and subsequent use of ACT was greatest among younger patients (p<0.001), with less co-morbidity (p=0.001) and from higher socioeconomic backgrounds (p=0.021). There was wide geographic variation in rates of referral to MO after cystectomy (range 26 to 59%, p<0.001); referral rates remained stable over time. Patients seen by MO in 2004-2008 were more likely to receive ACT (57%) compared to patients treated in earlier years (41% 1994-1998 and 46% 1999-2003, p<0.001). Conclusions: Lack of referral to MO appears to be an important barrier to use of NACT/ACT suggesting upstream decision-making by urologists is an important target in future knowledge translation. Low treatment rates among patients seen by MO suggest that therapy has also not been wholly embraced by MOs or patients.
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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 | 0.001 |
| 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 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".