Abstracts of the 45th Congress of the Société Internationale d’Urologie
Bibliographic record
Abstract
The SIU wishes to extend its gratitude to the urologists who contributed their time to review abstract submissions for the 45th SIU Congress:Refaat Abusamra, Libya;Sanjai Addla, India;Kinju Adhikari, India;Neeraj Agarwal, United States;Madhu Agrawal, India;Sachin Agrawal, United Kingdom;Thomas Ahlering, United States;Shusuke Akamatsu, Japan;Peter Albers, Germany;Salah Albuheissi, United Kingdom;Naif Alhathal, Saudi Arabia;Bedeir Ali-El-Dein, Egypt;Murtadha Almusafer, Iraq;Anastasios Anastasiadis, Greece;Mohamed Arafa, Qatar;Amandeep Arora, India;Zeeshan Aslam, United Kingdom;Hammad Ather, Pakistan;Widi Atmoko, Indonesia;Melanie Aubé-Peterkin, QC;Riccardo Autorino, United States;Ben Ayres, United Kingdom;Puskal Kumar Bagchi, India;Ganesh Bakshi, India;Mevlana Derya Balbay, Turkey;Neil Barber, United Kingdom;John Barry, United States;Jens Bedke, Germany;Elisa Berdondini, Italy;Gajanan Bhat, India;Amit Bhattu, United States;Naeem Bhojani, Canada;N I Bhuiyan, Bangladesh;Marta Bizic, Serbia;Damien Bolton, Australia;Vincenzo Borgna, Chile;Muhammad Bulbul, Lebanon;Gian Maria Busetto, Italy;Ana Gabriela Caballero Garcia, Mexico;Adam Calaway, United States;Amparo Camacho, United States;Kevin Campbell, United States;Francesco Capelan, Switzerland;Manuel Castanheira de Oliveira, Portugal;Christine Joy Castillo, Philippines;David Castro-Diaz, Spain;Arun Chawla, India;Manohar ChikkaMoga Siddaiah, India;Archil Chkhotua, Georgia;Sung Yong Cho, Korea, Rep [...]
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.029 |
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".