1869 UTILIZATION AND IMPACT OF PERI-OPERATIVE CHEMOTHERAPY FOR MUSCLE-INVASIVE BLADDER CANCER: A POPULATION-BASED OUTCOMES STUDY
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Metastatic Disease + Staging1 Apr 20131869 UTILIZATION AND IMPACT OF PERI-OPERATIVE CHEMOTHERAPY FOR MUSCLE-INVASIVE BLADDER CANCER: A POPULATION-BASED OUTCOMES STUDY D. Siemens, Gavin Li, Paul Peng, Ian Tannock, Ahmed Elharram, Weidong Kong, William Mackillop, and Chris Booth D. SiemensD. Siemens Kingston, Canada More articles by this author , Gavin LiGavin Li Kingston, Canada More articles by this author , Paul PengPaul Peng Kingston, Canada More articles by this author , Ian TannockIan Tannock Tornoto, Canada More articles by this author , Ahmed ElharramAhmed Elharram Kingston, Canada More articles by this author , Weidong KongWeidong Kong Kingston, Canada More articles by this author , William MackillopWilliam Mackillop Kingston, Canada More articles by this author , and Chris BoothChris Booth Kingston, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.2288AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The utilization of perioperative chemotherapy for bladder cancer in the general population and the survival benefit associated with therapy is not well described. Here we report practice patterns and outcomes associated with peri-operative chemotherapy in the general population of Ontario, Canada. METHODS Electronic records of treatment were linked to the population-based Ontario Cancer Registry to identify all patients who underwent cystectomy for bladder cancer in Ontario 1994-2008; surgical pathology reports were obtained to identify cases with muscle-invasive disease. Utilization of neoadjuvant (NACT) and adjuvant (ACT) chemotherapy was compared across 3 study periods: 1994-98, 1999-03, 2004-08. Logistic regression was used to analyze factors associated with use of NACT/ACT. A Cox model and propensity score analysis was used to explore the association between ACT and survival. RESULTS In 1994-2008 4876 patients underwent cystectomy; surgical pathology reports were identified for 3429 cases of which 2738 had muscle-invasive disease. While use of NACT did not change substantially over the 3 study periods (5%, 3%, 6%; p=0.004), utilization of ACT increased with time (16%, 19%, 23%; p=0.001). In adjusted analyses younger age and less co-morbidity were associated with greater utilization of NACT and ACT. T3/T4 tumors (OR 2.1, 95%CI 1.6-2.8), node positive disease (OR 7.2, 95%CI 5.5-9.5), and presence of lymphovascular invasion (OR 1.7, 95%CI 1.2-2.3) were associated with greater utilization of ACT. While there was no substantial variation in utilization of NACT across geographic regions (range 3% to 5%), regional use of ACT varied considerably (range 12% to 31%). Five year overall (OS) and cancer-specific survival (CSS) for all muscle-invasive cases were 30% (95%CI 28-31%) and 34% (95%CI 32-36%) respectively. In Cox analysis T3/T4 tumors (HR 1.7, 95%CI 1.6-2.0), node positive disease (HR 1.9, 95%CI 1.7-2.1), and lymphovascular invasion (HR 1.8, 95%CI 1.6-2.1) were independently associated with inferior OS. Utilization of ACT was associated with improved OS (HR 0.70, 95%CI 0.6-0.8) and improved CSS (HR 0.70, 95%CI 0.6-0.8). CONCLUSIONS Despite accumulating evidence and guidelines, NACT/ACT remains substantially underutilized in routine clinical practice. Our results suggest that ACT is associated with a substantial survival benefit in the general population. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189Issue 4SApril 2013Page: e766 Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.MetricsAuthor Information D. Siemens Kingston, Canada More articles by this author Gavin Li Kingston, Canada More articles by this author Paul Peng Kingston, Canada More articles by this author Ian Tannock Tornoto, Canada More articles by this author Ahmed Elharram Kingston, Canada More articles by this author Weidong Kong Kingston, Canada More articles by this author William Mackillop Kingston, Canada More articles by this author Chris Booth Kingston, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".