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Record W4383302991 · doi:10.1111/1556-4029.15321

Subnuclear renal tubular vacuoles in alcohol use disorder

2023· article· en· W4383302991 on OpenAlexaff
Finn Morgan Auld, Jaqueline L. Parai, Christopher M. Milroy

Bibliographic record

VenueJournal of Forensic Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsVacuoleAlcoholChemistryMedicineBiochemistry

Abstract

fetched live from OpenAlex

Subnuclear vacuoles in the proximal renal tubules have been reported as a histologic sign of ketoacidosis. Originally described in diabetic ketoacidosis, renal vacuoles can be found in other ketogenic states such as alcoholic ketoacidosis (AKA), starvation, and hypothermia, underpinned by deranged fatty acid metabolism. A retrospective analysis of 133 deaths associated with alcohol use disorder (AUD) examined at autopsy between 2017 and 2020 was undertaken. This study aimed to determine the prevalence of subnuclear vacuoles in deaths of those with AUD and their specificity for deaths from AKA, and to elucidate what demographic, biochemical, and pathologic findings are associated with subnuclear vacuoles. In each case, vitreous humor biochemistry including electrolytes, glucose, and beta-hydroxybutyrate (BHB) was analyzed alongside postmortem hemoglobin A1c and renal and liver histology. Renal histology was graded for the presence of vacuoles as absent (0), scanty (1), or easily identifiable (2). Liver histology was graded for steatosis and for fibrosis if Masson trichrome staining was available. Vacuoles were commonly seen in the deaths of those with AUD. They were seen in deaths due to AKA but were not specific to that cause of death. With vacuoles present, lower vitreous sodium (139 vs. 142 mmol/L; p = 0.005), higher vitreous BHB (1.50 vs. 1.39 mmol/L; p = 0.04), severe hepatic steatosis, and severe hepatic fibrosis were seen, compared with those without renal vacuoles.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.317
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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