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T. Yildirim, Resul Yılmaz, M. Altindal, E. Turkmen, M. Arici, B. Altun, Y. Erdem, O. Guliyev, M. Erkmen Uyar, E. Tutal, Z. Bal, S. Sezer, U. Bal, B. Say n, B. Erdemir, A. O'Rourke-Potowki, Nathan Gauge, Hugo A. Penny, A. Cronin, S. Frame, David J. Goldsmith, J. A. Yagan, A. Chandraker, Radmila M. Velickovic Radovanovic, Aleksandra Catić Djordjević, B. Mitic, N. Stefanovic, T. Cvetkovic, N. Serpieri, F. Grosjean, Giuseppe Sileno, Martina Torreggiani, Vincenzo Esposito, Filippo Mangione, M. Abelli, F. Castoldi, Davide Catucci, C. Esposito, A. Dal Canton, A. V. Vatazin, Aleksei Zulkarnaev, C. Borst, Yunqi Liu, J. Thoning, Martin Tepel, C. Libetta, E. Margiotta, I. Borettaz, M. Canevari, Claudia Martinelli, Elisa Lainu, Federica Meloni, V. Sepe, Rui M. Costa, E. Vasquez Martul, Juan Mosquera Reboredo, César Rivera, Francesca Simonato, G. Tognarelli, G. Daidola, E. Gallo, Manuel Burdese, V. Cantaluppi, L. Biancone, G. P. Segoloni, M. Priora, M. Messina, M. Tamagnone, Antonio Linsalata, A. Lavacca, G. Segoloni, Willij C. Zuidema, Ruud A.M. Erdman, J. van de Wetering, F. Dor, Joke I. Roodnat, E. Massey, L. Timmerman, J. IJzermans, W. Weimar, C. Sibley-Allen, Rachel Hilton, M. Moghul, Lisa Burnapp, Glenn Blake, T. Y. Koo, J.-S. Park, Hoon Cheol Park, G.-H. Kim, C. H. Lee, I. H. Oh, C. M. Kang, J. K. Hwang, Sunghyeon Park, B. S. Choi, H. J. Chun, Ji‐il Kim, C. W. Yang, In Seok Moon, Steven Van Laecke, W. Van Biesen, Evi Nagler, Youri Taes, Patrick Peeters, R. Vanholder, R. Pruthi, R. Ravanan, A. Casula, Mark Harber, P. Roderick, Damian Fogarty, A. Cho, Jungho Shin, H. R. Jang, J. E. Lee, W. Huh, D. J. Kim, Ha Young Oh, Y.-G. Kim, Asunción Sancho, J. Kanter Berga, Sandra Beltrán Catalán, Ana Isabel Avila Bernabeu, L.M. Pallardó Mateu, E. Gonzalez, N. Polanco, M. Molina, E. Gutierrez, L. Garcia Puente, A. Sevillano, E. Morales, M. Praga, A. Andres, M. Banasik, M. Boratynska, Katarzyna Kościelska−Kasprzak, D. Bartoszek, M. Myszka, S. Zmonarski, Beata Nowakowska, E. 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Mallat, Johan W. de Fijter, Yu Ah Hong, C. W. Park, Y.-S. Kim, G. Suleymanlar, Z. Uzundurukan, A. Kapuagas, İrfan Şencan, Recep Akdağ, A. Torio, Vicente Más, M. J. Perez-Saez, M. Mir, A. Faura, O. Montes-Ares, M. D. Checa, D. Sawinski, J. Trofe-Clark, Tracy Sparkes, Priyanka Patel, S. Goral, Roy D. Bloom, H. J. Kim, S. J. Park, T. H. Kim, Y. W. Kim, Y. H. Kim, S. W. Kang, M. Abdel Halim, O. Gheith, Torki Al-Otaibi, A. Mosaad, W. Awadeen, T. Said, Prasad Nair, M.R.N. Nampoory
Bibliographic record
VenueNephrology Dialysis Transplantation · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineTransplantationIntensive care medicineSurgery
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
Opus teacher head0.021
GPT teacher head0.307
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical
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".
Quick stats
Citations0
Published2013
Admission routes1
Has abstractno