Who Will Fail Local Therapy for Renal Cell Carcinoma
Bibliographic record
Abstract
No AccessJournal of UrologyEditorial1 Jun 2008Who Will Fail Local Therapy for Renal Cell Carcinomais companion ofPositive Surgical Margins at Partial Nephrectomy: Predictors and Oncological OutcomesPreoperative Nomogram Predicting 12-Year Probability of Metastatic Renal Cancer Michael A.S. Jewett, and Alvaro Zuniga Michael A.S. JewettMichael A.S. Jewett More articles by this author , and Alvaro ZunigaAlvaro Zuniga More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2008.03.124AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Who Will Fail Local Therapy for Renal Cell Carcinoma." The Journal of Urology, 179(6), pp. 2087–2088 References 1 : Surgical factors influence bladder cancer outcomes: a cooperative group report. J Clin Oncol2004; 22: 2781. Google Scholar 2 : Do margins matter?: The prognostic significance of positive surgical margins in radical prostatectomy specimens. J Urol2005; 174: 903. Link, Google Scholar 3 : Long-term results of nephron sparing surgery for localized renal cell carcinoma: 10-year followup. J Urol2000; 163: 442. Link, Google Scholar 4 : Five-year survival after surgical treatment for kidney cancer: a population-based competing risk analysis. Cancer2007; 109: 1763. Google Scholar 5 : The natural history of observed enhancing renal masses: meta-analysis and review of the world literature. J Urol2006; 175: 425. Link, Google Scholar 6 : The natural history of incidentally detected small renal masses. Cancer2004; 100: 738. Google Scholar 7 : The natural history of small renal masses: a prospective multi-center Canadian trial. J Urol2007; 177: 169. abstract 509. Link, Google Scholar 8 : The natural history of untreated renal masses. BJU Int2007; 99: 1203. Google Scholar 9 : Positive surgical parenchymal margin after laparoscopic partial nephrectomy for renal cell carcinoma: oncological outcomes. J Urol2006; 176: 2401. Link, Google Scholar 10 : Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol2007; 25: 1316. Google Scholar 11 : Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. J Clin Oncol1999; 17: 2530. Google Scholar 12 : A preoperative clinical prognostic model for non-metastatic renal cell carcinoma. BJU Int2003; 92: 901. Google Scholar 13 : Prognostic assessment of nonmetastatic renal cell carcinoma: a clinically based model. Urology2001; 58: 141. Google Scholar 14 : Techniques, safety and accuracy of sampling of renal tumors by fine needle aspiration and core biopsy. J Urol2007; 178: 379. Link, Google Scholar 15 : Laparoscopic partial nephrectomy: 3-year followup. J Urol2006; 175: 459. Link, Google Scholar 16 : Solid renal tumors: an analysis of pathological features related to tumor size. J Urol2003; 170: 2217. Link, Google Scholar 17 : Renal cell carcinoma: prognostic significance of incidentally detected tumors. J Urol2000; 163: 426. Link, Google Scholar Division of Urology, Department of Surgical Oncology, Princess Margaret Hospital and the University Health Network, University of Toronto, Ontario, Canada© 2008 by American Urological AssociationFiguresReferencesRelatedDetailsRelated articlesJournal of UrologyApr 17, 2008, 12:00:00 AMPositive Surgical Margins at Partial Nephrectomy: Predictors and Oncological OutcomesJournal of UrologyApr 18, 2008, 12:00:00 AMPreoperative Nomogram Predicting 12-Year Probability of Metastatic Renal Cancer Volume 179Issue 6June 2008Page: 2087-2088 Advertisement Copyright & Permissions© 2008 by American Urological AssociationMetricsAuthor Information Michael A.S. Jewett More articles by this author Alvaro Zuniga More articles by this author Expand All 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.014 | 0.002 |
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".