Bibliographic record
Abstract
No AccessJournal of UrologyAdult Urology1 Oct 2022Editorial CommentThis article comments on the following:A Comparison of Percutaneous Ablation Therapy to Partial Nephrectomy for cT1a Renal Cancers: Results from the Canadian Kidney Cancer Information System Kyle M. Rose and Wade J. Sexton Kyle M. RoseKyle M. Rose Moffitt Cancer Center, Tampa, Florida More articles by this author and Wade J. SextonWade J. Sexton Moffitt Cancer Center, Tampa, Florida More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002798.02AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 208(4), p. 812 References 1. : Renal mass and localized renal cancer: AUA guideline. J Urol 2017; 198: 520 Link, Google Scholar 2. : Surveillance versus ablation for incidentally diagnosed small renal tumours: the SURAB feasibility RCT. Health Technol Assess 2017; 21: 1. Google Scholar 3. : Management of renal masses and localized renal cancer: systematic review and meta-analysis. J Urol 2016; 196: 989. Link, Google Scholar © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of Urology10 Jun 2022A Comparison of Percutaneous Ablation Therapy to Partial Nephrectomy for cT1a Renal Cancers: Results from the Canadian Kidney Cancer Information System Volume 208Issue 4October 2022Page: 812-812 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kyle M. Rose Moffitt Cancer Center, Tampa, Florida More articles by this author Wade J. Sexton Moffitt Cancer Center, Tampa, Florida 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 | 0.008 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.395 | 0.205 |
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".