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Record W4385299485 · doi:10.1016/j.ymgme.2023.107670

Acute hepatic porphyrias: Recommendations for diagnosis and management with real-world examples

2023· article· en· W4385299485 on OpenAlexaff
Akshata Moghe, Amy K. Dickey, Angelika Erwin, Rebecca Karp Leaf, Alan O’Brien, John G. Quigley, Manish Thapar, Karl E. Anderson

Bibliographic record

VenueMolecular Genetics and Metabolism · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPorphyrin Metabolism and Disorders
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersRecordati Rare DiseasesAlnylam Pharmaceuticals
KeywordsDiseaseMedicinePorphyriaAcute intermittent porphyriaIntensive care medicineGenetic testingGenetic counselingBioinformaticsInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

Acute hepatic porphyria (AHP) is a group of four rare inherited diseases, each resulting from a deficiency in a distinct enzyme in the heme biosynthetic pathway. Characterized by acute neurovisceral symptoms that may mimic other medical and psychiatric conditions, lack of recognition of the disease often leads to a delay in diagnosis and initiation of effective treatment. Biochemical testing for pathway intermediates that accumulate when the disease is active forms the basis for screening and establishing a diagnosis. Subsequent genetic analysis identifies the pathogenic variant, supporting screening of family members and genetic counseling. Management of AHP involves avoidance of known exogenous and hormonal triggers, symptomatic treatment, and prevention of recurrent attacks. Here we describe six case studies from our own real-world experience to highlight current recommendations and challenges associated with the diagnosis and long-term management of the disease.

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)0.008
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0030.008
Open science0.0040.003
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0080.006

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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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 designNot applicable
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

Citations12
Published2023
Admission routes1
Has abstractyes

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