1591 AGE, COMORBIDITIES, AND RACE ARE PREDICTORS TO UNDERGO RADICAL CYSTECTOMY AT LOW VOLUME INSTITUTIONS
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Invasive II1 Apr 20121591 AGE, COMORBIDITIES, AND RACE ARE PREDICTORS TO UNDERGO RADICAL CYSTECTOMY AT LOW VOLUME INSTITUTIONS Marco Bianchi, Maxine Sun, Jens Hansen, Nawar Hanna, Zhe Tian, Alberto Briganti, Shahrokh Shariat, Paul Perrotte, Francesco Montorsi, and Pierre Karakiewicz Marco BianchiMarco Bianchi Milan, Italy More articles by this author , Maxine SunMaxine Sun Montreal, Canada More articles by this author , Jens HansenJens Hansen Hamburg, Germany More articles by this author , Nawar HannaNawar Hanna Montreal, Canada More articles by this author , Zhe TianZhe Tian Montreal, Canada More articles by this author , Alberto BrigantiAlberto Briganti Milan, Italy More articles by this author , Shahrokh ShariatShahrokh Shariat New York, NY More articles by this author , Paul PerrottePaul Perrotte Montreal, Canada More articles by this author , Francesco MontorsiFrancesco Montorsi Milan, Italy More articles by this author , and Pierre KarakiewiczPierre Karakiewicz Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1364AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES We tested the hypothesis that old age, multiple comorbidities, and race may predict radical cystectomy at low volume institutions. METHODS Overall, 10991 patients treated with radical cystectomy for bladder cancer were identified amongst 1052 hospitals originating from the Nationwide Inpatient Sample, between years 1998 and 2007. We examined patient age, baseline Charlson comorbidity index (CCI), gender, race, hospital teaching status, hospital region, and annual household income according to hospital volume, which was modeled in a continuously coded fashion. Finally, we examined the effect of hospital volume on patient age and CCI, using linear regression analyses. Adjustment was made for all the aforementioned covariates. RESULTS The overall mean hospital volume was 8 cystectomies per year (median 4, interquartile range [IQR]: 2–8). First, hospital volume decreased with increasing age (≤59 years mean: 8.6 (median 4) vs. ≥80 years: 7.6 (median 4), P<0.001) and increasing CCI (0 mean: 8.6 (median 4) vs. ≥3: 6 (median 3), P<0.001). The effect of hospital volume also differed according to gender, hospital teaching status, and hospital region. Specifically, females, patients of black race, non-teaching hospitals, and hospitals located in the Midwest, were treated at institutions with the lowest hospital volume. In univariable linear regression analyses, decreasing age (beta: -0.038, P<0.001) and decreasing CCI (beta: -0.039, P<0.001) were inversely associated with increasing hospital volume. These findings were confirmed in multivariable analyses, where patients with increasing age (beta: -0.022, P=0.03) and higher CCI (beta: -0.028, P=0.005) were more likely to be operated at hospitals with a low hospital volume. CONCLUSIONS Our data show that advanced age, multiple comorbidities, black race, and female gender are predictor of radical cystectomy at low volume institution. Clustering of patients with those characteristics at low volume institutions does not appear to be incidental and may predispose to worse outcomes © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e644 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Marco Bianchi Milan, Italy More articles by this author Maxine Sun Montreal, Canada More articles by this author Jens Hansen Hamburg, Germany More articles by this author Nawar Hanna Montreal, Canada More articles by this author Zhe Tian Montreal, Canada More articles by this author Alberto Briganti Milan, Italy More articles by this author Shahrokh Shariat New York, NY More articles by this author Paul Perrotte Montreal, Canada More articles by this author Francesco Montorsi Milan, Italy More articles by this author Pierre Karakiewicz Montreal, 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 | 0.000 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.010 |
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".