1402-P: Functional Data Analysis Reveals Differences in Temporal Glucose Profiles Hidden by Standard Continuous Glucose Monitoring Analysis: A Secondary Analysis of the CONCEPTT Trial
Bibliographic record
Abstract
Background: The CONCEPTT randomized trial of CGM in pregnant women with type 1 diabetes showed significant improvement in pregnancy outcomes, but no difference in mean CGM glucose between those allocated to CGM vs. capillary glucose monitoring. Whilst summary statistics are recommended for the reporting of CGM data, they do not give dynamic information about the glucose profile across 24 hours. Aim: To determine, using novel functional data analysis (FDA), if differences in the temporal glucose profile occurred in women randomized to CGM. Methods: CGM data was available from 200 pregnant women (100 CGM intervention; 100 control) in the CONCEPTT study, at baseline, 24 and 34 weeks gestation. FDA was applied to the glucose data to generate functional glucose curves and multivariable statistical analysis applied. Results: Despite having the same mean CGM glucose as those on capillary glucose monitoring alone 121 vs. 126 mg/dL; 6.7 vs. 7.0 mmol/l p=0.14, FDA revealed that pregnant CGM users were in fact running a significantly lower glucose (by 9-14mg/dL; 0.5-0.8 mmol/l) for a total of 8 hours a day (08.30-12.30 and 15.00-19.00). Conclusion: Analysis of temporal glucose profiles gives key information about glucose control, missed by commonly reported CGM metrics. The women allocated to CGM ran a significantly lower glucose for 33% of the day. 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
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".