Bibliographic record
Abstract
No AccessJournal of UrologyAdult Urology1 Feb 2022Editorial CommentThis article comments on the following:An Algorithm to Personalize Nerve Sparing in Men with Unilateral High-Risk Prostate Cancer Nathan Perlis Nathan PerlisNathan Perlis Urologic Oncology, Princess Margaret Cancer Centre, Sprott Department of Surgery, University Health Network, University of Toronto, Toronto, Ontario, Canada More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002205.01AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 207(2), p. 357 Reference 1. : Multifocality and prostate cancer detection by multiparametric magnetic resonance imaging: correlation with whole-mount histopathology. Eur Urol 2015; 67: 569. Google Scholar © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of UrologySep 22, 2021, 12:00:00 AMAn Algorithm to Personalize Nerve Sparing in Men with Unilateral High-Risk Prostate Cancer Volume 207Issue 2February 2022Page: 357-357 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nathan Perlis Urologic Oncology, Princess Margaret Cancer Centre, Sprott Department of Surgery, University Health Network, University of Toronto, Toronto, Ontario, Canada 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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.398 | 0.233 |
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".