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Record W2807379796 · doi:10.1177/1093526618780776

Electron Microscopy Can Still Have a Role in the Diagnosis of Selected Inborn Errors of Metabolism

2018· article· en· W2807379796 on OpenAlexaff
Mana Taweevisit, Paul S. Thorner

Bibliographic record

VenuePediatric and Developmental Pathology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPrenatal diagnosisDiseaseMedicineChorionic villiMitochondrial diseaseBiopsyGenetic testingInherited diseaseElectron microscopePathologyInborn error of metabolismPediatricsPregnancyFetusMitochondrial DNAInternal medicineBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Many anatomic pathology laboratories no longer have electron microscopy facilities. A retrospective review of autopsies was performed to identify cases of inborn errors of metabolism (IEM) and determine the contribution of electron microscopy in making the diagnosis in those cases. Over a period of 17 years, there were 900 perinatal and pediatric autopsies. There were 7 cases (1%) of IEM, including 4 cases of Pompe disease, 1 case of I-cell disease, 1 case of bile acid synthesis defect, and 1 case of mitochondrial disease (Leigh syndrome). Electron microscopy was important in the diagnosis of I-cell disease and Pompe disease in our series. This technique enabled a prenatal diagnosis to be made from a chorionic villus biopsy in 2 cases with a positive family history. In less developed countries where upfront genetic testing may be too expensive and may need international referral, electron microscopy can still be useful for diagnosis of IEM, providing an affordable alternative with a more rapid turnaround time compared to gene mutation analysis or enzyme assay. Results can be used both for patient management and as a screen for which cases might benefit from genetic testing.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.421

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 head0.005
GPT teacher head0.240
Teacher spread0.234 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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