Use of Maternal GHb Concentration to Estimate the Risk of Congenital Anomalies in the Offspring of Women with Prepregnancy Diabetes
Bibliographic record
Abstract
OBJECTIVE: We sought to determine the absolute risk of having a congenital anomaly in relation to periconceptional GHb concentration among women with prepregnancy diabetes. RESEARCH DESIGN AND METHODS: Two reviewers independently retrieved all cohort studies through a systematic literature search between January 1985 and May 2006. For each study, the absolute risk of having a pregnancy affected by a major or minor structural anomaly (diagnosed either antenatally or up to 28 days after conception) was calculated according to the number of SDs of GHb above the mean for nondiabetic, nonpregnant control subjects. A multilevel logistic-normal model was used to pool the data, which were expressed in tabular and graphic formats. RESULTS: In seven cohort studies, there were 117 anomalies among 1,977 pregnancies. At a periconceptional GHb concentration 0 SD above normal, the absolute risk of a pregnancy affected by a congenital anomaly was approximately 2% (95% CI 0.0-4.4). At 2 SD above normal, the risk was 3% (0.4-6.1), and at 8 SD it was approximately 10% (2.3-17.8). For each 1-SD unit increase in GHb, the associated risk of a congenital malformation increased by an odds ratio of 1.2 (95% CI 1.1-1.4). The risk in relation to A1C followed the same pattern. CONCLUSIONS: Using data from a limited number of published studies, a practical aid was developed to optimize use of the GHb and A1C concentrations for estimating the absolute risk of a congenital anomaly in the offspring of women with prepregnancy diabetes.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| 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) | 0.001 | 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".