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Record W4220843685 · doi:10.2215/cjn.14181021

Overview of Diagnostic Criteria and Epidemiology of Acute Kidney Injury and Acute Kidney Disease in the Critically Ill Patient

2022· review· en· W4220843685 on OpenAlexaff
Bethany C. Birkelo, Neesh Pannu, Edward D. Siew

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2022
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineAcute kidney injuryCritically illIntensive care medicineEpidemiologyKidney diseaseDiseaseKidneyInternal medicine

Abstract

fetched live from OpenAlex

by William Heberden (1), AKI has remained a prominent complication of critical illness. Beyond KRT, treatment has been limited by the capacity to phenotype this condition. Here, we chronicle the evolution of attempts to classify AKI, including the adoption of consensus definitions, the expansion of diagnosis and prognosis with novel biomarkers, and emerging tools such as artificial intelligence (AI).

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)0.006
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0000.001
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
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 head0.146
GPT teacher head0.496
Teacher spread0.351 · 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
GenreReview

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

Citations48
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of the American Society of NephrologySame topicAcute Kidney Injury ResearchFrench-language works237,207