Managing diabetes during pregnancy. Guide for family physicians.
Bibliographic record
Abstract
OBJECTIVE: To provide a guide family physicians can use to interpret current evidence on treating women with pregestational and gestational diabetes mellitus (GDM) and to develop a model for managing these patients. QUALITY OF EVIDENCE: A MEDLINE search from January 1980 to December 2002 found randomized controlled trials (RCTs) and descriptive studies that had conflicting results regarding screening recommendations. Studies of intensive insulin therapy were predominantly large RCTs (level I evidence). Glycemic targets and guidelines for monitoring pregnant women are based primarily on consensus statements from large national societies. MAIN MESSAGE: Most pregnant women should be screened for GDM. Good glycemic control during pregnancy reduces congenital anomalies and stillbirths. Women failing to meet glycemic targets should be referred to multidisciplinary teams and considered for insulin therapy. Intensive insulin therapy reduces the risk of macrosomia and might reduce cesarean section rates and other serious outcomes. CONCLUSION: Despite controversy, family physicians can follow a plan for managing diabetic patients during pregnancy that is supported by the best available evidence.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.032 |
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".