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Record W1524117811 · doi:10.1111/ped.12450

<scp>P</scp>elizaeus–<scp>M</scp>erzbacher disease: Cellular pathogenesis and pharmacologic therapy

2014· review· en· W1524117811 on OpenAlexaff
Tomohiro Torii, Yuki Miyamoto, Junji Yamauchi, Akito Tanoue

Bibliographic record

VenuePediatrics International · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsHealth Research Foundation
FundersJapan Society for the Promotion of ScienceJapan Health Sciences Foundation
KeywordsMedicineLeukodystrophyMutationPathogenesisPhenotypeDiseaseBioinformaticsInternal medicineGeneGeneticsBiology

Abstract

fetched live from OpenAlex

Pelizaeus-Merzbacher disease (PMD) is a rare leukodystrophy that causes severe dysmyelination in the central nervous system in infancy and early childhood. Many previous studies showed that various proteolipid protein 1 (plp1) mutations, including duplications, point mutations, and deletions, lead to oligodendrocyte dysfunction in patients with PMD. PMD onset and clinical severity range widely, depending on the type of plp1 mutation. Patients with PMD exhibit a delayed mental and physical development phenotype, but specific pharmacological therapy and clinical treatment for PMD are not yet well established. This review describes PMD pathology and establishment of new clinical treatment for PMD. These findings support the development of a new therapy for PMD and these treatments may improve the quality of life in patients with PMD.

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)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.026
GPT teacher head0.313
Teacher spread0.287 · 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
GenreReview

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

Citations48
Published2014
Admission routes1
Has abstractyes

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