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2012· article· en· W2314071996 on OpenAlexaff
Rajit K. Basu, Michael Zappitelli, Hector R. Wong, Derek S. Wheeler, Lakhmir S. Chawla, Stuart L. Goldstein

Bibliographic record

VenueCritical Care Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsMontreal Children's Hospital
Fundersnot available
KeywordsMedicineAcute kidney injuryCreatinineInternal medicineReceiver operating characteristicIncidence (geometry)Intensive care unitRetrospective cohort study

Abstract

fetched live from OpenAlex

Introduction: Earlier detection of acute kidney injury (AKI) may expedite therapy and improve outcomes; we examined our previously described empiric concept of Renal Angina (ANG), which identifies children (pts) at-risk for AKI in the pediatric intensive care unit (PICU) (Goldstein, CJASN 2010). Hypothesis: We hypothesized that the presence of ANG determined by the renal angina index (RAI, ANG = RAI > 8), a composite of known AKI risk factors and graded thresholds of early clinical AKI signs (range 1-40), improves prediction of subsequent severe AKI (doubling of serum creatinine from baseline) at 72 hours (72h-AKI) over the absence of ANG (RAI < 8) and that ANG precision would be further improved by inclusion of serum AKI biomarkers neutrophil gelatinase associated lipocalin (NGAL), matrix metalloproteinase-8 (MMP-8), and neutrophil elastase-2 (Ela-2). Methods: A retrospective and prospective observational study of four individual cohorts of PICU pts (C1-C4) from two separate large tertiary institutions. Results: 72h-AKI incidence in C1-C4 was notable (13-21%). ANG presence was high in all four cohorts (15-68%). In C1 (derivation) (147 pts), the area under the receiver operating curve characteristic (AUC-ROC) of RAI for 72h-AKI was 0.73 (95% CI, 0.66-0.79). The negative predictive value (NPV) of ANG for absence of 72h-AKI was 94% (95% CI, 94-98). C2 and C3 were used as validation cohorts. In C2 (retrospective, 108 pts) the AUC-ROC of RAI for 72hr-AKI was 0.81 (95% CI, 0.71-0.91) and the NPV for ANG was 99% (95% CI, 92-100) while in C3 (prospective, 118 pts) the AUC-ROC of RAI for 72hr-AKI was 0.74 (95% CI, 0.59-0.81) and the NPV for ANG was 95% (95% CI, 89-98). Finally, in C4 (215 pts), inclusion of serum biomarkers NGAL, MMP-8, and Ela-2 with RAI improves all of the metrics. The specificity of ANG improves (36% to ~ 60% for each) and, by multiple logistic regression, the AUC-ROC for RAI significantly improved for 72h-AKI (0.80 to 0.84-0.86, p < 0.05 for each). Conclusions: We derived, validated and applied the renal angina construct for AKI prediction and suggest that renal angina enhances predictive precision for AKI, directing biomarker measurements to “rule out” AKI only in pts with renal angina.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.423
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.5770.463

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 head0.069
GPT teacher head0.445
Teacher spread0.377 · 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.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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