Survey of Endourology
Bibliographic record
Abstract
Section Editor Howard N. Winfield, M.D. West Alabama Urology Associates, Tuscaloosa, Alabama Laparoscopy Howard N. Winfield, M.D. West Alabama Urology Associates, Tuscaloosa, Alabama Tadashi Matsuda, M.D. Kansai Medical University, Osaka, Japan Ureteroscopy Michael J. Conlin, M.D. Oregon Health and Science University, Portland, Oregon Francis X. Keeley, Jr., M.D. British Urological Institute, Bristol, United Kingdom Percutaneous Surgery Robert Marcovich, M.D. University of Texas, San Antonio, Texas Michael Y.C. Wong, MBBS Singapore Urology and Fertility Centre, Singapore Extracorporeal Shock Wave Lithotripsy Geert G. Tailly, M.D. AZ Klina, Brasschaat, Belgium Ryan F. Paterson, M.D. University of British Columbia, Vancouver, Canada Transurethral Procedures Alexis E. Te, M.D. Cornell University, New York, New York Anup Patel, M.D. Imperial School of Medicine, London, United Kingdom Medical Aspects of Endourology Dean G. Assimos, M.D. Wake Forest University, Winston-Salem, North Carolina Hans-Gö ran Tiselius, M.D., Ph.D. Karolinska University Hospital, Stockholm, Sweden Investigative Endourology David A. Duchene, M.D. University of Kansas Medical Center, Kansas City, Kansas Maurice S. Michel, M.D., Ph.D. University Hospital Mannheim, Mannheim, Germany Robotics David I. Lee, M.D. Penn Presbyterian Medical Center, Philadelphia, Pennsylvania Ashok K. Hemal, M.S., M.Ch. Wake Forest University, Winston-Salem, North Carolina Thermal/Ablative Technology Surena F. Matin, M.D. M.D. Anderson Cancer Center, Houston, Texas Osamu Ukimura, M.D., Ph.D. Kyoto Prefectural University of Medicine, Kyoto, Japan
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".