Diabetes after pregnancy prevention trials: Systematic review for core outcome set development
Bibliographic record
Abstract
Diabetes prevention intervention studies in women with previous gestational diabetes have increased, but no consensus exists on core outcomes to support comparisons and synthesis of findings. We aimed to systematically catalogue outcomes in diabetes after pregnancy prevention interventions with the goal of developing a core outcome set. Embase, Medline, Cochrane Library, Cochrane Pregnancy and Childbirth Trials Register, and CINAHL were searched from inception to October 2017. Post-partum lifestyle and diabetes screening intervention studies in women with previous gestational diabetes and/or their families were eligible. No limits were placed on intervention type, duration, or location. Two authors independently screened and performed data extraction on outcomes, measurement tools, and relevant study characteristics. We analysed data from 38 studies (29 randomised controlled trials and 9 pre-post intervention evaluations) comprising 12,509 participants. Most publications (80%) occurred between the years 2012 and 2017. Among 172 outcomes, we identified 36 outcome groups and classified them under three domains: health status (body weight, body composition, diabetes risk, cardiometabolic risk, diabetes development, mental health, pregnancy outcomes, and fitness), health behaviours (dietary, physical activity, diabetes screening, behaviour change, and breastfeeding), and intervention processes (implementation). The health status domain contained the most commonly reported outcomes, but measurement tools were very heterogeneous. Despite the recent explosion in diabetes after pregnancy prevention studies, large variation in outcomes and measurement methods exists. Research is needed to define a core outcome set to standardise diabetes after pregnancy prevention interventions. The core outcome set should engage a wide group of stakeholders to identify impactful indicators for future trials.
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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 | 0.074 | 0.246 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.020 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".