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Record W4393141917 · doi:10.3390/livers4020012

Serendipity in Medicine-Elevated Immunoglobulin E Levels Associated with Excess Alcohol Consumption

2024· article· en· W4393141917 on OpenAlexaff
Stephen Malnick, Ali Abdullah, Fadi Ghanem, Sheral Ohayon Michael, Manuela G. Neuman

Bibliographic record

VenueLivers · 2024
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSerendipityAlcohol consumptionAntibodyAlcoholConsumption (sociology)MedicinePsychologyChemistryImmunologySociologyPhilosophyBiochemistrySocial science

Abstract

fetched live from OpenAlex

Making a diagnosis of alcoholic liver disease is not always easy. There are problems in obtaining an accurate and reliable history of alcohol consumption. Laboratory findings and hepatic imaging studies are neither sensitive or specific, and newer test are being considered. Recently, a patient was admitted with possible alcoholic hepatitis. The first-year resident who admitted the patient mistakenly ordered a blood test for serum IgE. The result was a markedly elevated −6440 IU/mL. There was no evidence of parasitic infections, atopy or autoimmune disease nor was there any eosinophilia. A literature search showed that elevated IgE levels are associated with alcohol abuse. This association has been forgotten and does not appear in standard reference sources such as UptoDate or Harrison’s Principles of Internal Medicine. This judicious use of examining serum IgE levels may aid in the diagnosis of alcoholic hepatitis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.165
GPT teacher head0.403
Teacher spread0.237 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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