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Record W4211256461 · doi:10.1016/j.eclinm.2022.101294

The life course perspective of gestational diabetes: An opportunity for the prevention of diabetes and heart disease in women

2022· review· en· W4211256461 on OpenAlexaff
Jennifer Fu, Ravi Retnakaran

Bibliographic record

VenueEClinicalMedicine · 2022
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineGestational diabetesDiabetes mellitusPregnancyDiseaseIntensive care medicineType 2 diabetesHeart diseaseNatural historyPerspective (graphical)Intervention (counseling)ObstetricsPediatricsGestationInternal medicineEndocrinologyNursing

Abstract

fetched live from OpenAlex

Gestational diabetes mellitus (GDM), which has traditionally been defined as glucose intolerance of varying severity with first onset in pregnancy, is rising in prevalence with maternal hyperglycemia currently affecting one in every six pregnancies worldwide. Although often perceived as a medical complication of pregnancy, GDM is actually a chronic cardiometabolic disorder that identifies women who have an elevated lifetime risk of ultimately developing type 2 diabetes and cardiovascular disease. In identifying high-risk women early in the natural history of these conditions, the diagnosis of GDM raises the tantalizing possibility of early intervention and risk modification. However, before such promise can be realized in practice, a series of clinical challenges/obstacles (reviewed herein) must be overcome. Ultimately, the coupling of this life course perspective of GDM with concerted efforts to overcome these challenges may enable fulfilment of this unique opportunity for the primary prevention of diabetes and heart disease in women.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.127
GPT teacher head0.453
Teacher spread0.327 · 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

Citations85
Published2022
Admission routes1
Has abstractyes

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