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Record W2948868125 · doi:10.2337/db19-1407-p

1407-P: Laboratory Glycemic Markers vs. Continuous Glucose Monitoring (CGM) for Prediction of Neonatal Outcomes in Type 1 Diabetes Pregnancy—An Ancillary Study of the CONCEPTT Trial

2019· article· en· W2948868125 on OpenAlexaboutno aff
Claire L. Meek, Diana Tundidor, Helen Murphy, Jennifer M. Yamamoto, Eleanor Scott, Dong-Dong Ma, José A. Halperin, Denice S. Feig

Bibliographic record

VenueDiabetes · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsGlycemicMedicineNeonatal hypoglycemiaPregnancyContinuous glucose monitoringType 1 diabetesHypoglycemiaLogistic regressionNeonatal intensive care unitDiabetes mellitusGestational ageObstetricsType 2 diabetesPediatricsInternal medicineGestational diabetesGestationEndocrinology

Abstract

fetched live from OpenAlex

Type 1 diabetes (T1D) in pregnancy is associated with increased neonatal morbidity, which improves with optimal glycemic control. Aim: To compare lab and CGM glucose summary measures as predictors of neonatal outcomes in T1D pregnancy. Methods: 225 CONCEPTT participants had 6-day CGM and blood analysis of glycemic markers in 1st trimester, 24 and 34 weeks (Average glucose; % time in target 63-140 mg/dl, coefficient of variation (CV)); HbA1c; glycated CD59 (gCD59); 1,5-anhydroglucitol (1,5AG); glycated albumin). Outcomes: large for gestational age (LGA), neonatal hypoglycemia (NH) and neonatal intensive care unit (NICU) admission. Statistics: Unadjusted logistic regression. Results: All glucose summary measures excluding CV predicted neonatal outcomes (Table). Glycemic control at all timepoints from 1st trimester was important for LGA, but emerged later for NH (24 and 34 weeks) and NICU (mainly 24 weeks). Both CGM time in target and average glucose and lab markers HbA1c, 1,5AG and gCD59 predicted all three outcomes studied. Time in target was the best CGM predictor. The best lab predictors were HbA1c, 1,5AG and gCD59. HbA1c was the strongest predictor of LGA and NH, but only predicted NICU admission late in pregnancy. Conclusions: In women with T1D, both CGM and lab glucose summary measures can predict neonatal outcomes from 1st trimester. Disclosure C.L. Meek: None. D. Tundidor: None. H.R. Murphy: Advisory Panel; Self; Medtronic MiniMed, Inc. J.M. Yamamoto: None. E.M. Scott: Advisory Panel; Self; Abbott. Speaker's Bureau; Self; Abbott, Eli Lilly and Company. D. Ma: None. J. Halperin: Stock/Shareholder; Self; Mellitus, LLC. D. Feig: Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Medtronic. R. Corcoy: None. Funding JDRF; Canadian Clinical Trials Network; National Institute for Health Research; European Foundation for the Study of Diabetes/Sanofi; Diabetes UK (17/0005712 to C.L.M.); Asahi Kasei Pharma Corporation; GlycoMark, Inc.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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