Long-Term Effects of a Diabetes and Pregnancy Program
Bibliographic record
Abstract
OBJECTIVE: To determine whether women with pregestational diabetes obtained long-term benefits from an intensive diabetes management program during pregnancy. RESEARCH DESIGN AND METHODS: Women with pregestational diabetes who had attended an intensive diabetes management program in pregnancy between 1991 and 1999 were interviewed regarding diabetes self-management behaviors and current glycemic control. A retrospective chart review was done to obtain information on self-management behaviors at entry to the program and at delivery and compared with the present. RESULTS: Comparing entry to the program to delivery, all diabetes self-management behaviors improved significantly, including frequency of self-monitoring of blood glucose, frequency of insulin injections, and frequency and complexity of insulin dose adjustment (IDA). HbA(1c) (A1C) also improved significantly from entry to delivery (mean 0.073-0.060) (P < 0.0001). Comparing entry to the present, frequency of insulin injections improved significantly (P = 0.0004), frequency of IDA improved significantly (P = 0.004), and complexity of IDA improved significantly (P = 0.0032). However, there was no significant change in frequency of self-monitoring of blood glucose (P = 0.766) from before pregnancy to the present, and A1C significantly worsened by 0.015 (P < 0.0001, 95% CI 0.009-0.021) from entry to the program to the present. CONCLUSIONS: Women participating in an intensive diabetes management program during pregnancy improve significantly from entry to delivery in diabetes self-management behaviors and glycemic control and, in the long term, retain some of these behaviors and knowledge. However, this is not reflected in an improved A1C level. This may be explained by the loss of contact with the diabetes care team and/or the discontinuation of frequent self-monitoring of blood glucose-a critical behavior necessary for achieving optimal glycemic control.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".