1315 DISPARITIES IN ACCESS TO HIGH-VOLUME SURGEONS AND HOSPITALS AMONGST NON-METASTATIC KIDNEY CANCER PATIENTS TREATED WITH PARTIAL OR RADICAL NEPHRECTOMY
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Localized (II)1 Apr 20131315 DISPARITIES IN ACCESS TO HIGH-VOLUME SURGEONS AND HOSPITALS AMONGST NON-METASTATIC KIDNEY CANCER PATIENTS TREATED WITH PARTIAL OR RADICAL NEPHRECTOMY Andreas Becker, Hugo Lavigueur-Blouin, Florian Roghmann, Zhe Tian, Al'a Abdo, Malek Meskawi, Markus Graefen, Pierre I. Karakiewicz, Quoc-Dien Trinh, and Maxine Sun Andreas BeckerAndreas Becker Hamburg, Germany More articles by this author , Hugo Lavigueur-BlouinHugo Lavigueur-Blouin Montreal, Canada More articles by this author , Florian RoghmannFlorian Roghmann Herne, Germany More articles by this author , Zhe TianZhe Tian Montreal, Canada More articles by this author , Al'a AbdoAl'a Abdo Montreal, Canada More articles by this author , Malek MeskawiMalek Meskawi Montreal, Canada More articles by this author , Markus GraefenMarkus Graefen Hamburg, Germany More articles by this author , Pierre I. KarakiewiczPierre I. Karakiewicz Montreal, Canada More articles by this author , Quoc-Dien TrinhQuoc-Dien Trinh Montreal, Canada More articles by this author , and Maxine SunMaxine Sun Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.2669AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Previous literature has indicated a relationship between surgical and hospital volume and postoperative complications in several procedures. We sought to examine and compare access to high volume surgeons and hospitals amongst non-metastatic kidney cancer patients treated with either partial (PN) or radical nephrectomy (RN). METHODS Relying on the Florida Inpatient Database, 16634 non-metastatic renal cell carcinoma patients treated with PN or RN were identified between years 1998 and 2008. Distribution of patients according to age, comorbidities, race, gender, and insurance status was examined according to high-volume surgeons and hospitals (top 75th percentile). Generalized estimating equations for prediction of access to high-volume surgeons and hospitals were performed. Sub-analyses comprised of open PN, laparoscopic PN, open RN, and laparoscopic RN. RESULTS The majority of patients underwent open RN (76%) followed by open PN (15%). Laparoscopic RN and PN were given to respectively 8.3 and 1.0%. High surgical and hospital volume was defined as ≥10 and ≥33 nephrectomies per year, respectively. After accounting for all covariates, Medicare beneficiaries were 23% less likely to be treated by high-volume surgeons (P<0.001), while Medicaid recipients were 39% more likely to be treated by high-volume surgeons (P=0.001). Sicker individuals were less likely to be operated by high-volume surgeons (odds ratio: 0.97, P=0.01). Laparoscopy was more likely to be offered by high-volume surgeons (OR: 3.85, P<0.001). No statistically significant difference was recorded with respect to gender, race, and age. When access to high-volume hospitals was examined, Medicare patients were 24% less likely to be treated at a high-volume hospital, whereas Medicaid patients were 40% more likely to be treated at a high-volume hospital (both P≤0.001). Black race was associated with 13% lower odds of high-volume hospital access. Advancing age (OR: 0.95, P<0.001) and increasing baseline conditions (OR: 0.99, P=0.008) were both inversely related to high-volume hospitals. Similarly disparities were recorded after stratification according to open vs. laparoscopic PN/RN. CONCLUSIONS In the context that high surgical and hospital volume has been linked to better postoperative outcomes following nephrectomy, our results indicate that not all segments of the population will benefit from such quality of care. Some sociodemographic characteristics are consistently linked with lower use of high-volume surgeon or hospital. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189 Issue 4S April 2013 Page: e537 Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.Metrics Author Information Andreas Becker Hamburg, Germany More articles by this author Hugo Lavigueur-Blouin Montreal, Canada More articles by this author Florian Roghmann Herne, Germany More articles by this author Zhe Tian Montreal, Canada More articles by this author Al'a Abdo Montreal, Canada More articles by this author Malek Meskawi Montreal, Canada More articles by this author Markus Graefen Hamburg, Germany More articles by this author Pierre I. Karakiewicz Montreal, Canada More articles by this author Quoc-Dien Trinh Montreal, Canada More articles by this author Maxine Sun 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".