The reproductive health of daughters of pregestational diabetic women: Medical Birth Registry of Norway
Bibliographic record
Abstract
Maternal diabetes may have an impact upon a daughter's reproductive health through genetic influences, an altered fetal metabolic environment or both. We examined the reproductive health of daughters of diabetic women using linked generation data from the Medical Birth Registry of Norway. Among all female births between 1967 and 1982 (n = 459182), 739 had a mother with registered pregestational diabetes, a rate of 1.6 per 1000 deliveries. A total of 142904 daughters delivered at least one child by 1998. After taking into account differences in survival, we observed no differences in the percentage of childbearing and in the average number of children born by 1998 between daughters with and without a diabetic mother in age-stratified analyses. In analyses limited to singleton deliveries and stratified by mothers' and daughters' diabetic status, we found a threefold excess stillbirth delivery rate among women who had either a mother with pregestational diabetes (2.6%) or pregestational diabetes themselves (2.6%) compared with the stillbirth delivery rate observed in non-diabetic women with no maternal history of diabetes (0.8%). These findings were unaltered in multivariable analyses adjusting for daughters' maternal age and registered obstetric risk factors. Our results indicate that pregestational diabetes remains a health care challenge in Norway and that further evaluation of the reproductive health of daughters of diabetic pregnancies is warranted.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".