1403-P: Functional Data Analysis Reveals Differences in Temporal Glucose Profiles Associated with Large for Gestational Age: A Secondary Analysis of the CONCEPTT Study
Bibliographic record
Abstract
Background: Continuous glucose monitoring (CGM) provides far greater detail about fetal exposure to maternal glucose across the 24 hour day, but recommended summary statistics give no dynamic information about the glucose profile across 24 hours. Aim: To examine whether differences in temporal glucose control occur in women with type 1 diabetes who develop large for gestational age infants (LGA). Research Design and Methods: CGM data was available from 200 pregnant women (100 CGM intervention; 100 controls) in the CONCEPTT study, at baseline, 24 and 34 weeks gestation. All women were being treated to tight glycemic targets (mean HbA1c 6.4% at 34 weeks gestation). Functional data analysis (FDA) was applied to determine differences in temporal glucose profiles. LGA was defined as birth weight ≥90th percentile adjusted for infant sex, gestational age, maternal BMI, ethnicity and parity. Results: 122/200 women had a LGA infant (61%). FDA revealed that those women who gave birth to a LGA infant ran a significantly higher glucose (by 9-14mg/dL; 0.5-0.8 mmol/l) for 16.5 hours of the day compared to those women who did not have a LGA infant (see fig). Conclusion: Women who go on to have LGA infants have significantly higher fetal exposure to glucose across the 24 hour day, despite being treated to tight glucose targets. Disclosure E.M. Scott: Advisory Panel; Self; Abbott. Speaker's Bureau; Self; Abbott, Eli Lilly and Company. H.R. Murphy: Advisory Panel; Self; Medtronic MiniMed, Inc. D. Feig: Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Medtronic. G. Law: None. Funding JDRF; Canadian Clinical Trials Network; National Institute for Health Research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".