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Record W2125216251 · doi:10.1542/neo.13-7-e420

Nephrotoxic Medication Exposure and Acute Kidney Injury in Neonates

2012· article· en· W2125216251 on OpenAlex
Michael Zappitelli, David T. Selewski, David J. Askenazi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeoReviews · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsNephrotoxicityMedicineAcute kidney injuryIntensive care medicineIncidence (geometry)PharmacologyKidneyInternal medicine

Abstract

fetched live from OpenAlex

Nephrotoxic medication use is common in neonates. In older children, the use of nephrotoxic medication is known to be one of the most common causes of acute kidney injury (AKI) and to be associated with increased morbidity. In critically ill neonates, AKI significantly complicates fluid and electrolyte management and may be an important risk factor for mortality. Better understanding of methods to avoid and detect the presence of nephrotoxicity may lead to more intelligent use of these medications, which could ultimately reduce the incidence of AKI and improve outcomes. In this work, we summarize why neonates are predisposed to drug nephrotoxicity, review the mechanisms and clinical picture of the most common nephrotoxic medications used in neonates (aminoglycosides, vancomycin, amphotericin B, acyclovir, nonsteroidal anti-inflammatory drugs, and radiocontrast agents), and discuss the roles of angiotensin-converting enzyme inhibitors and diuretics in nephrotoxicity. We also suggest ways to avoid and reduce the incidence and complications of neonatal nephrotoxicity.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.356
Teacher spread0.329 · 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