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Record W2991066370 · doi:10.21037/atm.2019.11.12

Does elevated urinary Dkkopf-3 level predict vulnerability to kidney injury during cardiac surgery?

2019· letter· en· W2991066370 on OpenAlexaff
Matthew B. Lanktree, York Pei

Bibliographic record

VenueAnnals of Translational Medicine · 2019
Typeletter
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity Health NetworkSt. Joseph’s Healthcare HamiltonUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineAcute kidney injuryRenal replacement therapyRenal functionHemodialysisUrine outputMechanical ventilationCardiac surgeryUrinary systemIntensive care medicineUrologyCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) occurs in approximately one quarter of patients undergoing cardiac surgery and is associated with increased short-term and long-term mortality, as well as prolonged time for mechanical ventilation, intensive care, and hospitalization (1,2). AKI is defined by a sudden decrease in glomerular filtration rate typically with reduced urine output over a time period of hours to days; many patients with AKI also require renal replacement therapy (RRT) including hemodialysis or continuous RRT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.114
GPT teacher head0.381
Teacher spread0.267 · 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
GenreCommentary

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
Published2019
Admission routes1
Has abstractyes

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