Bibliographic record
Abstract
No AccessJournal of UrologyOriginal Research Articles1 Apr 2024Editorial CommentThis article comments on the following:Automated Identification of Key Steps in Robotic-Assisted Radical Prostatectomy Using Artificial Intelligence Daniel T. Keefe Daniel T. KeefeDaniel T. Keefe View All Author Informationhttps://doi.org/10.1097/JU.0000000000003859AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 211(4), p. 585 REFERENCES 1. . Trends in medical artificial intelligence publications from 2000-2020: where does radiology stand?. Open J Clin Med Images. 2022; 2(2):1052. Crossref, Google Scholar 2. Automated identification of key steps in robotic-assisted radical prostatectomy using artificial intelligence. J Urol. 2024; 211(4):575-584. Link, Google Scholar 3. . Standardized reporting of machine learning applications in urology: the STREAM-URO framework. Eur Urol Focus. 2021; 7(4):672-682. Crossref, Medline, Google Scholar © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of Urology24 Jan 2024Automated Identification of Key Steps in Robotic-Assisted Radical Prostatectomy Using Artificial Intelligence Volume 211Issue 4April 2024Page: 585-585 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Daniel T. Keefe 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 | 0.056 |
| 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 | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.418 | 0.256 |
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".