Re: Improved Prediction of Long-Term, Other Cause Mortality in Men With Prostate Cancer
Bibliographic record
Abstract
No AccessJournal of UrologyLetter to the Editor/Erratum1 May 2012Re: Improved Prediction of Long-Term, Other Cause Mortality in Men With Prostate CancerT. J. Daskivich, K. Chamie, L. Kwan, J. Labo, A. Dash, S. Greenfield and M. S. Litwin J Urol 2011; 186: 1868–1873 Quoc-Dien Trinh, Khurshid R. Ghani, Mani Menon, Maxine Sun, and Pierre I. Karakiewicz Quoc-Dien TrinhQuoc-Dien Trinh More articles by this author , Khurshid R. GhaniKhurshid R. Ghani More articles by this author , Mani MenonMani Menon More articles by this author , Maxine SunMaxine Sun More articles by this author , and Pierre I. KarakiewiczPierre I. Karakiewicz More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.12.121AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Re: Improved Prediction of Long-Term, Other Cause Mortality in Men With Prostate Cancer." The Journal of Urology, 187(5), p. 1931 References 1 : A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis1987; 40: 373. Google Scholar Vattikuti Urology Institute, Henry Ford Health System, Detroit, MichiganCancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Quebec, Canada© 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 5May 2012Page: 1931 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Quoc-Dien Trinh More articles by this author Khurshid R. Ghani More articles by this author Mani Menon More articles by this author Maxine Sun More articles by this author Pierre I. Karakiewicz More articles by this author Expand All Advertisement PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".