194-OR: Impaired Awareness of Hypoglycemia and Maternal/Neonatal Outcomes in the CONCEPTT Trial
Bibliographic record
Abstract
Background: Patients with diabetes and impaired awareness of hypoglycemia (IAH) are at increased risk of severe hypoglycemia (SH). IAH is increased during pregnancy. To date, maternal and neonatal outcomes of pregnant women with hypoglycemia unawareness or SH has not been well described. Aim: To examine maternal and neonatal outcomes in women with IAH compared to women with normal awareness of hypoglycemia (NAH). Methods: We included 214 pregnant women with type 1 diabetes who participated in the CONCEPTT trial. Participants completed Hypoglycemia Fear Surveys (HFS-II) at baseline and study completion. Post-hoc analysis of data was conducted using logical regression analysis to estimate the odds of occurrence of events by IAH versus NAH. The role of continuous glucose monitoring (CGM) use on outcomes was also examined. Results: Overall, 30% of participants reported IAH (n=64). Women with IAH had more episodes of SH during pregnancy (mean 0.44 vs. 0.08, p<0.001). Women with IAH scored significantly higher on the HFS-II worry scale (25.9 vs. 20.1, p 0.0036), indicating more fear of hypoglycemia at baseline. They also spent more time below range (<63 mg/dl) at 12 weeks gestation. Higher overall HFS-II scores were associated with a higher risk of maternal SH episodes (RR 1.78, 95% CI 1.39-2.27) and a lower incidence of neonatal hypoglycemia (OR 0.597, 95% CI 0.385-0.893). There were no other differences in obstetric/neonatal outcomes between women with IAH and NAH. Higher HFS-II scores were associated with more diabetes-related complications, such as nephropathy (OR 1.907, 95% CI 1.06-3.4), independent of duration of diabetes. Finally, CGM did not affect incidence of SH (RR 0.54, 95% CI 0.12 - 2.16). Conclusion: In pregnant women with type 1 diabetes, IAH is associated with more maternal SH episodes and more fear of hypoglycemia. Fear of hypoglycemia was associated with reduced neonatal hypoglycemia as well as more maternal diabetes-related complications. Disclosure J. Bahrami: None. G. Tomlinson: None. H.R. Murphy: Advisory Panel; Self; Medtronic. D. Feig: Advisory Panel; Self; Novo Nordisk A/S. Speaker’s Bureau; Self; Medtronic. Funding JDRF; Canadian Clinical Trial Network
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".