Correction: International Alliance of Urolithiasis (IAU) consensus on miniaturized percutaneous nephrolithotomy
Bibliographic record
Abstract
After publication of the article, it was brought to our attention that the author name Otas Durutovic was duplicated in the author list and the second one should be replaced by the author Chu Ann Chai, and the affiliations of Ji-Wen Cheng and Chu Ann Chai are incorrect, the correct author list with correct affiliations is shown below: Guo-Hua Zeng, Wen Zhong, Giorgio Mazzon, Wei Zhu, Sven Lahme, Sanjay Khadgi, Janak Desai, Madhu Agrawal, David Schulsinger, Mantu Gupta, Emanuele Montanari, Juan Manuel Lopez Martinez, Shabir Almousawi, Vincent Emanuel F. Malonzo, Seshadri Sriprasad, Otas Durutovic, Vimoshan Arumuham, Stefania Ferretti, Wissam Kamal, Ke-Wei Xu, Fan Cheng, Xiao-Feng Gao, Ji-Wen Cheng, Bhaskar Somani, Mordechai Duvdevani, Kah Ann Git, Christian Seitz, Norberto Bernardo, Tarek Ahmed Amin Ibrahim, Albert Aquino, Takahiro Yasui, Cristian Fiori, Thomas Knoll, Athanasios Papatsoris, Nariman Gadzhiev, Ulanbek Zhanbyrbekuly, Oriol Angerri, Hugo Lopez Ramos, Iliya Saltirov, Mohamad Moussa, Guido Giusti, Fabio Vicentini, Edgar Beltran Suarez, Margaret Pearle, Glenn M. Preminger, Qing-Hui Wu, Chu Ann Chai, Khurshid Ghani, Marcus Maroccolo, Marianne Brehmer, Palle J. Osther, Marek Zawadzki, Azimdjon Tursunkulov, Monolov Nurbek Kytaibekovich, Abdusamad Abdukakhorovich Abuvohidov, Cesar Antonio Recalde Lara, Zamari Noori, Stefano Paolo Zanetti, Sunil Shrestha, Jean de la Rosette, John Denstedt, Zhang-Qun Ye, Kemal Sarica & Simon Choong. The original publication has been updated.
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 | 0.006 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 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".