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Record W4401463116 · doi:10.1038/s41390-024-03436-5

ADVANCE: a biomedical informatics approach to investigate acute kidney injury in infants

2024· article· en· W4401463116 on OpenAlexfundno aff
Jennifer A. Rumpel, Sofia Perazzo, Jonathan P. Bona, Andrew M. South, Matthew W. Harer, Daniel Liu, Michelle C. Starr, Mona Khattab, Rachel Han, Cara Slagle, Eileen Ciccia, Tasnim Najaf, Matthew Gillen, Mimily Harsono, Arwa Nada, Kiran Dwarakanath, Semsa Gogcu, Tahagod Mohamed, Christine Stoops, Elizabeth Bonachea, Mary Revenis, Jessica Roberts, Robert Michael Lenzini, Anne Debuyserie, Catherine Joseph, Karna Murthy, Patricio E. Ray, Mario Schootman, Corey Nagel

Bibliographic record

VenuePediatric Research · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteHospital for Sick ChildrenConnecticut Children's Medical CenterChildren’s Hospital of Wisconsin Research InstituteChildren's of AlabamaChildren's Healthcare of AtlantaChildren's National HospitalSeattle Children's Research InstituteArkansas Biosciences InstituteUniversity of California, San FranciscoChildren's Hospital of PhiladelphiaNationwide Children's HospitalNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins UniversityChildren's Hospital of PittsburghNational Center for Advancing Translational SciencesChildren's Hospital ColoradoCincinnati Children's Hospital Medical CenterChildren's Hospital of MichiganTexas Children's Hospital
KeywordsAcute kidney injuryMedicineNeonatal intensive care unitMechanical ventilationIntensive care unitIntensive care medicineIntensive careInformaticsEmergency medicinePediatricsAnesthesiaInternal 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)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.053
GPT teacher head0.425
Teacher spread0.372 · 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

Citations1
Published2024
Admission routes1
Has abstractno

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