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Record W4411928113 · doi:10.1111/cge.70011

Expanding the Genetic and Phenotypic Spectrum of <scp> <i>POLRMT</i> </scp> ‐Related Mitochondrial Disease

2025· article· en· W4411928113 on OpenAlexaff
Mahmoud R. Fassad, Sebastian Valenzuela, Monika Oláhová, Jack J. Collier, Charlotte V. Y. Knowles, Eleni Mavraki, Miriam Elbracht, Nergis Güzel, T. Herberhold, Ingo Kurth, Andrea Maier, Larissa Mattern, Carol Saunders, Helen McCullagh, Katrin Õunap, Saskia B. Wortmann, André Reis, Lei Zhang, Claes M. Gustafsson, Robert McFarland, Robert W. Taylor

Bibliographic record

VenueClinical Genetics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersMedical Research CouncilWellcome Trust Centre for Mitochondrial ResearchKnut och Alice Wallenbergs StiftelsePathological Society of Great Britain and IrelandLily FoundationNational Institute for Health and Care ResearchSwedish Cancer FoundationLifeArcVetenskapsrådetEesti TeadusagentuurFight for Sight
KeywordsPhenotypeGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Mitochondrial diseases are a complex group of conditions exhibiting significant phenotypic and genetic heterogeneity. Genomic testing is increasingly used as the first step in the diagnostic pathway for mitochondrial diseases. We used next-generation sequencing followed by bioinformatic data analysis to identify potentially damaging variants in the POLRMT gene (NM_005035.4) in six new affected individuals. Structural protein analysis predicted the detrimental impact of variants on POLRMT protein structure. Patients show extended phenotypic abnormalities often presenting early in life with features including global developmental delay, cognitive impairment, short stature and muscular hypotonia. This study expands the genetic and phenotypic landscape of mitochondrial disease associated with POLRMT variants.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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
Published2025
Admission routes1
Has abstractyes

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