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Record W2990088683 · doi:10.1016/j.kint.2019.09.019

Epidemiology, molecular, and genetic methodologies to evaluate causes of CKDu around the world: report of the Working Group from the ISN International Consortium of Collaborators on CKDu

2019· editorial· en· W2990088683 on OpenAlexaff
Shuchi Anand, Ben Caplin, Marvin González-Quiroz, Stephen L. Schensul, Vivek Bhalla, Xavier Vela Parada, Nishantha Nanayakkara, Andrew Fire, Adeera Levin, David J. Friedman, Angie Aguilar‐González, Kevin Abbot, Thilak D. J. Abeysekara, Kerstin Amann, Gloria Ashuntantang, Daniel R. Brooks, Denis Chavarría, Christoph Daniel, Ricardo Correa‐Rotter, Marc De Broe, P. Mangala C.S. De Silva, José Ramón Fernández Domínguez, Kai‐Uwe Eckardt, Dorien Fader, Fred Finkelstein, Rebecca S. B. Fischer, Anirban Ganguli, Ramon Antonio Garcia Trabinho, Jason Glaser, Marvin Antonio Gonzalez Quiroz, Lalarukh Haider, D. Bruce Harris, Chulani Herath, Raúl Herrera, Anne Hradsky, Wendy E. Hoy, Kristina Jakobsson, Saroj Jayasinghe, Channa Jaysummana, Vivekanand Jha, Richard B. Johnson, Neeraja Kambham, Nishamani Karanasema, François Kaze, Paul L. Kimmel, Erik H. Koritzinsky, Robyn G. Langham, Laurent Le Bellego, Nathan Levin, Valerie Lyuckx, Magdalena Madero, Ekiti Martin, Charu Malik, Louise Moist, Marva Moxey‐Mims, Andrew S. Narva, Fabiana Baggio Nerbass, Dónal O’Donoghue, Carlos Orantes, Neil Pearce, Charaka Ratnayake, Prabheer Roy-Chaudhury, Agnese Ruggiero, Laura Gabriela Sánchez‐Lozada, Rajiv Saran, Luca Segantini, Isabelle Seksek, David Sheikh‐Hamad, Robert A. Star, Luisa Strani, Penny Vlahos, David H. Wegman, Ilana Weiss, Eranga Wijewickrama, Julia Wijkström, Paul M. Wise, Emily M. Wright, Chih‐Wei Yang, Karen Yeates

Bibliographic record

VenueKidney International · 2019
Typeeditorial
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of British Columbia
FundersMedical Research Council
KeywordsKidney diseaseEtiologyEpidemiologyMedicineDiseaseTollDeath tollSri lankaIntensive care medicineEnvironmental healthPathologyInternal medicineImmunologyHistoryEthnologySouth asia

Abstract

fetched live from OpenAlex
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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.382
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.053
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.379
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations26
Published2019
Admission routes1
Has abstractno

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