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Record W2136636339 · doi:10.1186/1824-7288-37-17

Acute interstitial nephritis with acetaminophen and alcohol intoxication

2011· article· en· W2136636339 on OpenAlex

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

Venue˜The œItalian Journal of Pediatrics/Italian journal of pediatrics · 2011
Typearticle
Languageen
FieldMedicine
TopicNephrotoxicity and Medicinal Plants
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAcetaminophenInterstitial nephritisAcute kidney injuryDrugDiseaseIntensive care medicinePharmacologyInternal medicineKidney

Abstract

fetched live from OpenAlex

Drug-induced acute interstitial nephritis (AIN) represents a growing cause of renal failure in current medical practice. While antimicrobials and non-steroidal anti-inflammatory drugs are typically associated with drug-induced AIN, few reports have been made on the involvement of other analgesics. We report our experience in managing a 17-year-old female with AIN and subsequent renal injury following an acetaminophen overdose in conjunction with acute alcohol intoxication. It is well established that acetaminophen metabolism, particularly at high doses, produces reactive metabolites that may induce renal and hepatic toxicity. It is also plausible however, that such reactive species could instead alter renal peptide immunogenicity, thereby inducing AIN. In the following report, we review a possible mechanism for the acetaminophen-induced AIN observed in our patient and also discuss the potential involvement of acute alcohol ingestion in disease onset. The objective of our report is to increase awareness of healthcare professionals to the potential involvement of these commonly used agents in AIN pathogenesis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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