MétaCan
Menu
Back to cohort
Record W3193365158 · doi:10.3390/ijns7030055

Detection of Early Onset Carnitine Palmitoyltransferase II Deficiency by Newborn Screening: Should CPT II Deficiency Be a Primary Disease Target?

2021· article· en· W3193365158 on OpenAlexaff
Rachel Mador-House, Zaiping Liu, Sarah Dyack

Bibliographic record

VenueInternational Journal of Neonatal Screening · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health Centre
Fundersnot available
KeywordsNewborn screeningMedicineCarnitineDecompensationPediatricsBiotinidase deficiencyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Early-onset carnitine palmitoyltransferase II deficiency (CPT II deficiency) (OMIM 600650) can result in severe outcomes, which are often fatal in the neonatal to infantile period. CPT II deficiency is a primary target in the Maritime Newborn Screening Program. We report a case of neonatal-onset CPT II deficiency identified through expanded newborn screening with tandem mass spectrometry. Identification through newborn screening led to early treatment interventions, avoidance of metabolic decompensation, and a better clinical outcome. Newborn screening for CPT II deficiency is highly sensitive and specific with no false positives identified. The only screen positive case detected identified a true positive case. This experience illustrates the importance of newborn screening for CPT II deficiency and demonstrates why reconsideration should be taken to add this disease as a primary newborn screening target.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Neonatal ScreeningSame topicMetabolism and Genetic DisordersFrench-language works237,207