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Record W4416695733 · doi:10.1080/25787489.2025.2588012

Acute kidney and liver injury requiring hemodialysis following cathinone use in a person with HIV

2025· article· en· W4416695733 on OpenAlexaff
Lucia Federica Stefanelli, Dorella Del Prete, Leda Cattarin, Elena Naso, Maria Loreta De Giorgi, Elena Sgrò, Francesca Martino, Annamaria Cattelan, Federico Nalesso, Maria Mazzitelli

Bibliographic record

VenueHIV Research & Clinical Practice · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsUniversity Hospital Foundation
FundersNemzeti Kutatási, Fejlesztési és Innovaciós AlapViiV HealthcareGilead Sciences
KeywordsContext (archaeology)HemodialysisCathinoneAdverse effectAcute kidney injuryHuman immunodeficiency virus (HIV)Toxicity

Abstract

fetched live from OpenAlex

INTRODUCTION: Chemsex drugs are psychoactive substances used to facilitate and enhance sexual activity. Their use is frequent among men who have sex with men (MSM). Synthetic cathinones have been the most common drugs recently used in chemsex practice. After their intake, the clinical spectrum may include tachycardia, hypertension, hallucinations, seizures, hyperthermia, rhabdomyolysis with resulting nephrotoxicity, and potentially life-threatening multiorgan failure. CASE PRESENTATION: Here, we report the case of a gentleman with HIV who came to observe severe rhabdomyolysis and acute kidney injury (AKI) following the use of Khat. CONCLUSION: This case highlights that clinicians need to be vigilant about the adverse effects of synthetic cathinones used in the context of chemsex as a possible cause of AKI. To the best of our knowledge, this is the first reported case of AKI caused by cathinone toxicity successfully treated in Italy.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.248
GPT teacher head0.558
Teacher spread0.309 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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