Bibliographic record
Abstract
No AccessJournal of UrologyPediatric Urology1 May 2023Editorial CommentThis article comments on the following:Deep Learning of Videourodynamics to Classify Bladder Dysfunction Severity in Patients With Spina Bifidais a letter which has replyReply By Authors Armando J. Lorenzo Armando J. LorenzoArmando J. Lorenzo Division of Pediatric Urology, Hospital for Sick Children, Toronto, Ontario, Canada Department of Surgery, University of Toronto, Toronto, Ontario, Canada More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003267.02AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "Editorial Comment." The Journal of Urology, 209(5), p. 1002 REFERENCES 1. . Would ChatGPT3 Get a Wharton MBA? A Prediction Based on Its Performance in the Operations Management Course. Mack Institute for Innovation Management at the Wharton School, University of Pennsylvania; 2023. Google Scholar 2. Deep learning of videourodynamics to classify bladder dysfunction severity in patients with spina bifida. J Urol. 2023; 209(5):994-1003. Link, Google Scholar 3. Machine learning for urodynamic detection of detrusor overactivity. Urology. 2022; 159:247-254. Crossref, Medline, Google Scholar 4. Translating pediatric urodynamics from clinic into collaborative research: lessons and recommendations from the UMPIRE study group. J Pediatric Urol. 2021; 17(5):716-725. Crossref, Medline, Google Scholar © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of Urology14 Feb 2023Deep Learning of Videourodynamics to Classify Bladder Dysfunction Severity in Patients With Spina BifidaJournal of Urology7 Apr 2023Reply By Authors Volume 209Issue 5May 2023Page: 1002-1002 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Armando J. Lorenzo Division of Pediatric Urology, Hospital for Sick Children, Toronto, Ontario, Canada Department of Surgery, University of Toronto, Toronto, Ontario, Canada 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 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 | 0.058 |
| 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 | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.333 | 0.190 |
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".