The Impact of Abnormal Glucose Tolerance and Obesity on Fetal Growth
Bibliographic record
Abstract
OBJECTIVE: Factors linked with insulin resistance were examined for their association with large-for-gestational-age (LGA) infant birth weight and gestational diabetes. STUDY DESIGN: Data came from a longitudinal cohort study of 2,305 subjects without overt diabetes, analyzed using multinomial logistic and linear regression. RESULTS: High maternal BMI (OR = 1.53 (1.11, 2.12)), height (1.98 (1.62, 2.42)), antidepressant use (1.71 (1.20, 2.44)), pregnancy weight-gain exceeding 40 pounds (1.79 (1.25, 2.57)), and high blood sugar (2.68, (1.53, 5.27)) were all positively associated with LGA birth. Strikingly, the difference in risk from diagnosed and treated gestational diabetes compared to women with a single abnormal glucose tolerance test (but no diagnosis of gestational diabetes) was significant (OR = 0.65, p = 0.12 versus OR = 2.84, p < 0.01). When weight/length ratio was used instead, different factors were found to be significant. BMI and pregnancy weight-gain were found to influence the development of gestational diabetes, through an additive interaction. CONCLUSIONS: High prepregnancy BM, height, antidepressant use, pregnancy weight-gain exceeding 40 pounds, and high blood sugar were associated with LGA birth, but not necessarily infant weight/length ratio. An additive interaction between BMI and pregnancy weight-gain influenced gestational diabetes development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".