1797 THE RISK OF IN-HOSPITAL MORTALITY AFTER CYTOREDUCTIVE NEPHRECTOMY: AN ASSESSMENT BASED ON PATIENT CHARACTERISTICS AND POSTOPERATIVE OUTCOMES DURING HOSPITALIZATION
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Advanced I1 Apr 20121797 THE RISK OF IN-HOSPITAL MORTALITY AFTER CYTOREDUCTIVE NEPHRECTOMY: AN ASSESSMENT BASED ON PATIENT CHARACTERISTICS AND POSTOPERATIVE OUTCOMES DURING HOSPITALIZATION Maxine Sun, Jens Hansen, Marco Bianchi, Quoc-Dien Trinh, Nawar Hanna, Shahrokh Shariat, Paul Perrotte, and Pierre Karakiewicz Maxine SunMaxine Sun Montreal, Canada More articles by this author , Jens HansenJens Hansen Hamburg, Germany More articles by this author , Marco BianchiMarco Bianchi Milan, Italy More articles by this author , Quoc-Dien TrinhQuoc-Dien Trinh Detroit, MI More articles by this author , Nawar HannaNawar Hanna Montreal, Canada 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 , and Pierre KarakiewiczPierre Karakiewicz Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1828AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Cytoreductive nephrectomy (CNT) may be considered in patients with metastatic renal cell carcinoma (mRCC). Our goal was to examine which baseline patient characteristics, as well as adverse intraoperative and postoperative events during hospitalization may be capable of quantifying the risk of in-hospital mortality (IHM). METHODS We relied on the Nationwide Inpatient Sample, a large contemporary population-based cohort originating from the United States, and identified 3300 mRCC patients treated with CNT between years 1998 and 2007. The rate of IHM was examined across patient age groups, baseline comorbidities, number of metastases, intraoperative and postoperative complications, length of stay, and blood transfusion. A logistic regression model was fitted for the rate of IHM. Following a backward variable selection, we identified the most informative and parsimonious variables for prediction of IHM, using the area under the curve cross-validation. RESULTS Overall IHM was 2.4%. The rate of IHM differed significantly according to patient age, blood transfusions, intraoperative complications, number of postoperative complications, as well as the presence of cardiac-, vascular-, hemorrhagic-, and/or accidental complications (Table 1). The integration of such variables resulted in a discriminant accuracy of 84% after cross-validation. CONCLUSIONS Intraoperative and postoperative adverse outcomes are highly informative in the prediction of IHM risk following surgery. This information may be useful for clinicians, hospital staff, and family members as a dynamic assessment risk for IHM. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e725 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Maxine Sun Montreal, Canada More articles by this author Jens Hansen Hamburg, Germany More articles by this author Marco Bianchi Milan, Italy More articles by this author Quoc-Dien Trinh Detroit, MI More articles by this author Nawar Hanna Montreal, Canada More articles by this author Shahrokh Shariat New York, NY More articles by this author Paul Perrotte Montreal, Canada 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".