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Record W1512266982 · doi:10.1002/bdra.20704

Understanding diabetic teratogenesis: Where are we now and where are we going?

2010· review· en· W1512266982 on OpenAlexaboutno aff
Sheller Zabihi, Mary R. Loeken

Bibliographic record

VenueBirth Defects Research Part A Clinical and Molecular Teratology · 2010
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsTeratologyNeural tubePregnancyDiseaseDiabetes mellitusMedicineEmbryonic stem cellType 2 diabetesNeural tube defectBioinformaticsPhysiologyBiologyEndocrinologyGeneticsGestationInternal medicineGeneFetusEmbryo

Abstract

fetched live from OpenAlex

Maternal pregestational diabetes (type 1 or type 2) poses an increased risk for a broad spectrum of birth defects. To our knowledge, this problem first came to the attention of the Teratology Society at the 14th Annual Meeting in Vancouver, B.C. in 1974, with a presentation by Lewis Holmes, "Etiologic heterogeneity of neural tube defects". Although advances in the control of diabetes in the decades since the discovery of insulin in the 1920's have reduced the risk for birth defects during diabetic pregnancy, the increasing incidence of diabetes among women of childbearing years indicates that this cause of birth defects is a growing public health concern. Major advances in understanding how a disease of maternal fuel metabolism can interfere with embryogenesis of multiple organ systems have been made in recent years. In this review, we trace the history of the study of diabetic teratogenesis and discuss a model in which tissue-specific developmental control genes are regulated at specific times in embryonic development by glucose metabolism. The major function of such genes is to suppress apoptosis, perhaps to preserve proliferative capability, and inhibit premature senescence.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.003
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 head0.271
GPT teacher head0.458
Teacher spread0.187 · 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.

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

Citations100
Published2010
Admission routes1
Has abstractyes

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