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Record W2625255161 · doi:10.1038/pr.2017.136

Developing a neonatal acute kidney injury research definition: a report from the NIDDK neonatal AKI workshop

2017· article· en· W2625255161 on OpenAlexafffund
Michael Zappitelli, Namasivayam Ambalavanan, David J. Askenazi, Marva Moxey‐Mims, Paul L. Kimmel, Robert A. Star, Carolyn Abitbol, Patrick D. Brophy, Guillermo Hidalgo, Mina Hanna, Catherine Morgan, Tonse N.K. Raju, Patricio E. Ray, Zayhara Reyes-Bou, Amani Roushdi, Stuart L. Goldstein

Bibliographic record

VenuePediatric Research · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsYork Central HospitalUniversity of AlbertaMcGill University Health CentreMontreal Children's Hospital
FundersLeonard M. Miller School of MedicineUniversity of AlbertaChildren's National HospitalNational Institute of Child Health and Human DevelopmentMcGill University Health CentreNational Institute of Diabetes and Digestive and Kidney DiseasesMcGill UniversityEast Carolina UniversityUniversity of MiamiNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsAcute kidney injuryMedicineIntensive care medicineInternal 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)0.233
metaresearch head score (Gemma)0.123
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2330.123
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.005
Science and technology studies0.0060.003
Scholarly communication0.0160.010
Open science0.0080.026
Research integrity0.0090.022
Insufficient payload (model declined to judge)0.0020.001

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 head0.260
GPT teacher head0.487
Teacher spread0.227 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations256
Published2017
Admission routes2
Has abstractno

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