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Record W2949192741 · doi:10.1016/j.ymgmr.2019.100483

Single-center experience with Beta-propeller protein-associated neurodegeneration (BPAN); expanding the phenotypic spectrum

2019· article· en· W2949192741 on OpenAlexaffabout
Marisa Chard, Juan Pablo Appendino, Luis Bello‐Espinosa, Colleen Curtis, Jong M. Rho, Xing‐Chang Wei, Walla Al‐Hertani

Bibliographic record

VenueMolecular Genetics and Metabolism Reports · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsHotchkiss Brain InstituteUniversity of CalgaryUniversity of SaskatchewanAlberta Children's HospitalRoyal University Hospital
Fundersnot available
KeywordsNeurodegenerationRett syndromeAutism spectrum disorderPhenotypePediatricsAutismMedicineEncephalopathyNeuroscienceCognitionEpilepsyBioinformaticsPsychologyDiseasePsychiatryPathologyGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Beta-propeller protein-associated neurodegeneration (BPAN) is a subtype of neurodegeneration with brain iron accumulation (NBIA) that presents with childhood developmental delay (especially speech delay), occasionally associated with epileptic encephalopathy, autism, or Rett-like syndrome. The majority of children described to date have been severely affected, with little to no expressive speech function, severe developmental delay, and cognitive impairment. Herein, five additional patients with BPAN identified in the same center in Canada are described, four with the typical severe phenotype and one with a milder phenotype. Our findings provide further evidence that a spectrum of severity exists for this rare and newly described condition. Challenges in identifying iron accumulation on brain MRI are also addressed. Additionally, the importance of including the WDR45 gene on epilepsy and Rett-like syndrome genetic panels is highlighted.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.225
Teacher spread0.207 · 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 designBench or experimental
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

Citations17
Published2019
Admission routes2
Has abstractyes

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