583 A POPULATION-BASED COMPETING-RISKS ANALYSIS OF SURVIVAL AFTER NEPHRECTOMY FOR RENAL CELL CARCINOMA
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Evaluation and Staging I1 Apr 2012583 A POPULATION-BASED COMPETING-RISKS ANALYSIS OF SURVIVAL AFTER NEPHRECTOMY FOR RENAL CELL CARCINOMA Marco Bianchi, Maxine Sun, Quoc-Dien Trinh, Jens Hansen, Zhe Tian, Umberto Capitanio, 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 , Quoc-Dien TrinhQuoc-Dien Trinh Detroit, MI More articles by this author , Jens HansenJens Hansen Hamburg, Germany More articles by this author , Zhe TianZhe Tian Montreal, Canada More articles by this author , Umberto CapitanioUmberto Capitanio Milan, Italy 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.2012.02.659AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Competing cause of mortality has been examined in patients with localized renal cell carcinoma (RCC). However the effect of tumor grade has not been accounted for. We reassessed this topic in all RCC stages integrating tumor grade. METHODS The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 42090 patients treated with NT between years 1988 and 2008. Patients were stratified in 32 strata according to age groups (≤59, 60-69, 70-79, and ≥80 years), Fuhrman grade (I-II vs. III-IV) and American Joint Committee on Cancer (AJCC) stage which resulted in a total of 32 combinations. Competing risk Poisson analyses were performed to simultaneously assess the rates of CSM and OCM at 5 years after nephrectomy. RESULTS Overall 11153 deaths occurred (27%). Of those, 5554 (50%) were due to CSM events. The risk of CSM and OCM at five years after nephrectomy is illustrated in Figure 1. Several findings were observed. First, amongst low-grade tumors, the highest CSM rates at five years were recorded in the youngest age group (≤59 years) with AJCC stage IV RCC (63%). In contrast, the highest OCM rate at five years were recorded in the oldest age group (≥80 years) with AJCC stage I RCC (33%). Not surprisingly, CSM rates increased with disease stage, while OCM rates increased with age. Second, amongst high-grade tumors, a similar trend was recorded where the highest CSM rate at five years were recorded in the youngest age group with AJCC stage IV RCC (79%), while the highest OCM rate at five years was recorded in the oldest age group with AJCC stage I RCC (44%). Finally, it is also noteworthy tumor grade was not particularly detrimental amongst patients with AJCC stage I RCC, especially in the elderly. For example, the five-year CSM rate in patients aged ≤80 years with AJCC stage I RCC was 7% for low-grade vs. 8% for high-grade disease. In contrast, for the same stage, the five-year CSM rate in patients aged ≤59 years was 2% for low-grade vs. 6% for high-grade disease. CONCLUSIONS Our study provides a valuable graphical aid for prediction of CSM, and OCM, according to patient age, disease stage and grade in patients treated with NT for RCC, and this can help clinicians to better stratify the risk-benefit ratio of NT. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187 Issue 4S April 2012 Page: e238 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.Metrics Author Information Marco Bianchi Milan, Italy More articles by this author Maxine Sun Montreal, Canada More articles by this author Quoc-Dien Trinh Detroit, MI More articles by this author Jens Hansen Hamburg, Germany More articles by this author Zhe Tian Montreal, Canada More articles by this author Umberto Capitanio Milan, Italy 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".