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Record W4307953389 · doi:10.1016/j.xkme.2022.100561

Semaglutide-Associated Acute Interstitial Nephritis: A Case Report

2022· article· en· W4307953389 on OpenAlexaff
Megan Borkum, Wynnie Lau, Paula Blanco, Myriam Farah

Bibliographic record

VenueKidney Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicNephrotoxicity and Medicinal Plants
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsSemaglutideMedicineKidney diseaseAcute kidney injuryIntensive care medicineRenal functionAdverse effectInternal medicineMedical prescriptionDiabetes mellitusEndocrinologyLiraglutideType 2 diabetesPharmacology

Abstract

fetched live from OpenAlex

Glucagon-like peptide 1 receptor agonists (GLP-1RAs) are being investigated to slow the decline of kidney function in type 2 diabetics with chronic kidney disease (CKD). These agents have proven benefits on cardiac outcomes and all-cause mortality as well as in reducing the incidence of macroalbuminuria. Ours is a case of drug-associated acute interstitial nephritis requiring hemodialysis temporally related to a semaglutide dose increase. This case is unique as the index patient had no underlying CKD. Limited cases of acute kidney injury, superimposed on underlying CKD, in patients taking the GLP-1RA semaglutide have been reported. To our knowledge, there are no existing case reports in the literature of GLP-1RA-associated acute interstitial nephritis in a patient with baseline normal kidney function. Because our prescription of these agents is increasing and is anticipated to increase further with growing scientific evidence for their benefit, we sought to highlight this possible, important serious adverse effect of semaglutide.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.002
Science and technology studies0.0040.002
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.298
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations14
Published2022
Admission routes1
Has abstractyes

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