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Record W2752404888 · doi:10.1016/s2352-4642(17)30069-x

Incidence and outcomes of neonatal acute kidney injury (AWAKEN): a multicentre, multinational, observational cohort study

2017· article· en· W2752404888 on OpenAlexaff
Jennifer G. Jetton, Louis Boohaker, Sidharth Kumar Sethi, Sanjay Wazir, Smriti Rohatgi, Danielle E. Soranno, Aftab S. Chishti, Robert P. Woroniecki, Cherry Mammen, Jonathan R. Swanson, Shanthy Sridhar, Craig S. Wong, Juan C. Kupferman, Russell Griffin, David J. Askenazi, David T. Selewski, Subrata Sarkar, Alison Kent, Jeffery Fletcher, Carolyn Abitbol, Marissa J. DeFreitas, Shahnaz Duara, Jennifer R. Charlton, Ronnie Guillet, Carl T. D’Angio, Ayesa Mian, Erin Rademacher, Maroun J. Mhanna, Rupesh Raina, Namasivayam Ambalavanan, Ayse Akcan‐Arikan, Christopher J. Rhee, Stuart L. Goldstein, Amy T. Nathan, Alok Bhutada, Shantanu Rastogi, Elizabeth Bonachea, Susan E. Ingraham, John D. Mahan, Arwa Nada, Patrick D. Brophy, Tarah T. Colaizy, Jonathan M. Klein, F. Sessions Cole, T. Keefe Davis, Joshua Dower, Lawrence S. Milner, Alexandra Smith, Mamta Fuloria, Kimberly J. Reidy, Frederick J. Kaskel, Jason Gien, Katja M. Gist, Mina Hanna, Sangeeta Hingorani, Michelle C. Starr, Catherine Joseph, Tara L. DuPont, Robin K. Ohls, Amy Staples, Surender Khokhar, Sofia Perazzo, Patricio E. Ray, Mary Revenis, Anne Synnes, Pia Wintermark

Bibliographic record

VenueThe Lancet Child & Adolescent Health · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesClinical and Translational Science Center, University of New MexicoClinical and Translational Science Institute, University of FloridaU.S. Food and Drug AdministrationNational Institutes of HealthNational Center for Research ResourcesInstitute of Clinical and Translational Sciences
KeywordsObservational studyIncidence (geometry)MedicineCohort studyAcute kidney injuryCohortEmergency medicineIntensive care medicinePediatricsInternal medicine

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 machine prediction

Teacher imitation

Not 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)0.003
metaresearch head score (Gemma)0.005
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.401
Teacher spread0.346 · 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 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

Citations725
Published2017
Admission routes1
Has abstractno

Explore more

Same venueThe Lancet Child & Adolescent HealthSame topicAcute Kidney Injury ResearchFrench-language works237,207