1293 ACTIVE SURVEILLANCE MAY INCREASE THE RISK OF CANCER-SPECIFIC MORTALITY RELATIVE TO PARTIAL OR RADICAL NEPHRECTOMY: A COMPETING-RISKS ANALYSIS
Bibliographic record
Abstract
You have accessJournal of UrologyKidney Cancer: Localized II1 Apr 20121293 ACTIVE SURVEILLANCE MAY INCREASE THE RISK OF CANCER-SPECIFIC MORTALITY RELATIVE TO PARTIAL OR RADICAL NEPHRECTOMY: A COMPETING-RISKS ANALYSIS Maxine Sun, Marco Bianchi, Jens Hansen, Quoc-Dien Trinh, Nawar Hanna, Markus Graefen, Francesco Montorsi, Paul Perrotte, and Pierre Karakiewicz Maxine SunMaxine Sun Montreal, Canada More articles by this author , Marco BianchiMarco Bianchi Milan, Italy More articles by this author , Jens HansenJens Hansen Hamburg, Germany 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 , Markus GraefenMarkus Graefen Hamburg, Germany More articles by this author , Francesco MontorsiFrancesco Montorsi Milan, Italy 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.2012.02.1627AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The current American Urological Association guidelines recommend active surveillance (AS) in selected patients for the management of small renal masses. We sought to assess and compare survival between surgical intervention relative to AS. METHODS Using the Surveillance, Epidemiology, and End Results database, patients with T1aN0M0 renal cell carcinoma (RCC), treated with partial nephrectomy (PN), radical nephrectomy (RN), or AS between 1988 and 2006 were abstracted. Since AS patients may differ from surgically managed patients, we relied on propensity-score matched analysis to circumvent the potential biases related to population differences. Competing-risks regression analyses predicting cancer-specific mortality (CSM), after accounting for other covariates, including other-cause mortality (OCM), were fitted. A sub-analysis was conducted in patients aged >75 years. RESULTS Overall, 1007 AS patients vs. 5935 and 13721 PN and RN patients were identified, respectively. Following propensity-score matched analysis, the five-year CSM rates, after adjusting for OCM, were 4.6 vs. 4.2 vs. 22.0% for PN, RN, and AS, respectively (P<0.001). In elderly patients (>75 years), the five-year CSM rates were 7.4 vs. 6.1 vs. 29.1% for the same groups, respectively (P<0.001). In competing-risks regression analyses, PN and RN patients were both 60% less likely to die of CSM than AS patients (both P<0.001), even after accounting for OCM. In patients >75 years, PN and RN individuals were 64 and 59% less likely to die of CSM than AS patients (both P<0.003). CONCLUSIONS Surgical management remains an important consideration in localized RCC, even in elderly patients (>75 years), despite accounting for OCM. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e524 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Maxine Sun Montreal, Canada More articles by this author Marco Bianchi Milan, Italy More articles by this author Jens Hansen Hamburg, Germany More articles by this author Quoc-Dien Trinh Detroit, MI More articles by this author Nawar Hanna Montreal, Canada More articles by this author Markus Graefen Hamburg, Germany More articles by this author Francesco Montorsi Milan, Italy 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".