1787 THE EFFECT OF NODAL CODING SCHEMES ON CANCER-SPECIFIC MORTALITY AFTER CYTOREDUCTIVE NEPHRECTOMY
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Advanced I1 Apr 20121787 THE EFFECT OF NODAL CODING SCHEMES ON CANCER-SPECIFIC MORTALITY AFTER CYTOREDUCTIVE NEPHRECTOMY Quoc-Dien Trinh, Jan Schmitges, Jesse D. Sammon, Khurshid R. Ghani, Maxine Sun, Jens Hansen, Wooju Jeong, Marco Bianchi, Jay Jhaveri, Shyam Sukumar, Paul Perrotte, Claudio Jeldres, Piyush K. Agarwal, Craig G. Rogers, James O. Peabody, Shahrokh F. Shariat, Mani Menon, and Pierre I. Karakiewicz Quoc-Dien TrinhQuoc-Dien Trinh Detroit, MI More articles by this author , Jan SchmitgesJan Schmitges Hamburg, Germany More articles by this author , Jesse D. SammonJesse D. Sammon Detroit, MI More articles by this author , Khurshid R. GhaniKhurshid R. Ghani Detroit, MI 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 , Wooju JeongWooju Jeong Detroit, MI More articles by this author , Marco BianchiMarco Bianchi Montreal, Canada More articles by this author , Jay JhaveriJay Jhaveri Detroit, MI More articles by this author , Shyam SukumarShyam Sukumar Detroit, MI More articles by this author , Paul PerrottePaul Perrotte Montreal, Canada More articles by this author , Claudio JeldresClaudio Jeldres Montreal, Canada More articles by this author , Piyush K. AgarwalPiyush K. Agarwal Detroit, MI More articles by this author , Craig G. RogersCraig G. Rogers Detroit, MI More articles by this author , James O. PeabodyJames O. Peabody Detroit, MI More articles by this author , Shahrokh F. ShariatShahrokh F. Shariat New York, NY More articles by this author , Mani MenonMani Menon Detroit, MI More articles by this author , and Pierre I. KarakiewiczPierre I. Karakiewicz Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1818AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Relatively few reports have described the outcomes of patients with node-positive renal cell carcinoma (RCC) in the presence of distant metastases. We examined the outcomes of these patients in a large population-based cohort of patients and examined the ability of standard risk factors to predict cancer-specific mortality (CSM). METHODS Using the Surveillance, Epidemiology, and End Results database, a total of 619 RCC patients with nodal and distant metastases undergoing cytoreductive nephrectomy were identified. Univariable and multivariable analyses addressed CSM with the intent of identifying independent predictors of CSM in this cohort of patients. Specifically, we examined the effect of the number of removed nodes (NRN), the number of positive nodes (NPN) and the percentage of positive nodes (PPN) on CSM. RESULTS Actuarial survival estimates demonstrated that 40.2, 23.5 and 11.5% of patients survived at 12, 24 and 60 months after nephrectomy. In Kaplan-Meier analyses, NRN failed to clearly discriminate between recorded CSM rates (log rank p=0.9). Discrimination was noted when CSM was stratified according to NPN (log rank p=0.002) and PPN (log rank p=0.003). In multivariable analyses, year of diagnosis, histological subtype and PPN were independent predictors of CSM. CONCLUSIONS Our data indicate that PPN is an independent predictor of CSM in patients with nodal and distant metastases undergoing cytoreductive nephrectomy. Consequently, PPN warrants consideration in future prognostic schemes. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e721 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Quoc-Dien Trinh Detroit, MI More articles by this author Jan Schmitges Hamburg, Germany More articles by this author Jesse D. Sammon Detroit, MI More articles by this author Khurshid R. Ghani Detroit, MI More articles by this author Maxine Sun Montreal, Canada More articles by this author Jens Hansen Hamburg, Germany More articles by this author Wooju Jeong Detroit, MI More articles by this author Marco Bianchi Montreal, Canada More articles by this author Jay Jhaveri Detroit, MI More articles by this author Shyam Sukumar Detroit, MI More articles by this author Paul Perrotte Montreal, Canada More articles by this author Claudio Jeldres Montreal, Canada More articles by this author Piyush K. Agarwal Detroit, MI More articles by this author Craig G. Rogers Detroit, MI More articles by this author James O. Peabody Detroit, MI More articles by this author Shahrokh F. Shariat New York, NY More articles by this author Mani Menon Detroit, MI More articles by this author Pierre I. 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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".