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Record W4400262289 · doi:10.1007/s00467-024-06437-y

Pediatric acute kidney injury and adverse health outcomes: using a foundational framework to evaluate a causal link

2024· review· en· W4400262289 on OpenAlexaff
Catherine Morgan, Emma Forest, E Ulrich, Scott M. Sutherland

Bibliographic record

VenuePediatric Nephrology · 2024
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineObservational studyIntensive care medicineAcute kidney injuryEpidemiologyMEDLINEKidney diseaseAdverse effectDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is a major global health problem, expensive to manage, and its associations with negative pediatric health outcomes have been clearly demonstrated. One of the most fundamental questions to consider as we use previous epidemiological information to advance research and care paradigms is the strength of the causal link between pediatric AKI and health outcomes. In this review, we apply the foundational framework of the Bradford Hill criteria to evaluate the extent to which a causal link exists between AKI and the associated adverse outcomes in children. Available data in children support a causal link between AKI and short-term outcomes including mortality, length of stay, and ventilation time. Clarifying the causal nature of longer term associations requires further high-quality observational studies in children, careful consideration of what defines the most meaningful and measurable longer term outcomes after pediatric AKI, and integration of evolving biological data related to mechanisms of disease. Preventing or mitigating AKI should lead to improved outcomes. Demonstrating such reversibility will solidify confidence in the causal relationship, improve child health, and highlight an aspect which is highly relevant to clinicians, scientists, and policy makers.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.087
GPT teacher head0.472
Teacher spread0.385 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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