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Record W2732518204 · doi:10.1139/bcb-2017-0080

Metabolomics and fetal alcohol spectrum disorder

2017· review· en· W2732518204 on OpenAlexaffvenue
Erin Goldberg, Michel Aliani

Bibliographic record

VenueBiochemistry and Cell Biology · 2017
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsMetabolomicsFetal Alcohol Spectrum DisorderMedicineFetal alcoholAlcohol consumptionBioinformaticsPregnancyBiologyAlcoholBiochemistry

Abstract

fetched live from OpenAlex

Fetal alcohol spectrum disorder (FASD) is a major public health issue that encompass an array of physical, neurological, and behavioral effects due to alcohol consumption during pregnancy. The classical biomarkers of FASD that are currently used lack sensitivity and specificity, and as such there is an opportunity through the use of novel metabolomics analysis to identify new biomarkers to identify those at risk for FASD, which could more effectively aid in early intervention. The focus of this minireview is to identify current work that is being done in the field of metabolomics in FASD in utero, and to highlight promising metabolites that could act as biomarkers in the future. We will conclude with suggestions for further research, as there is a large gap of knowledge in this particular area of metabolomics.

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)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.028
GPT teacher head0.313
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 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

Citations10
Published2017
Admission routes2
Has abstractyes

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