Pregnancy, delivery and neonatal outcomes among women with diabetic retinopathy
Bibliographic record
Abstract
Objectives Diabetic retinopathy is a common microvascular complication of diabetes. Despite that, there are few studies in the literature to address pregnancy, delivery, or neonatal outcomes among women with diabetic retinopathy.Methods We conducted a retrospective study using the Health Care Cost and Utilization Project-Nationwide Inpatient Sample Database over 11 years from 2004 to 2014. A delivery cohort was created using ICD-9 codes. ICD-9 code 250 or 249 was used to extract the cases of maternal diabetic retinopathy. A multivariant logistic regression model was used to adjust for statistically significant variables (p-value ≤ .05).Results There were a total of 9,096,788 deliveries during the study period. Of those, 86 615 pregnant women were found to have Diabetes Mellites (DM). Diabetic retinopathy was present in 1233 of the patients with DM. Diabetic retinopathy increased the likelihood of developing pregnancy-induced HTN (p < .0001), Preeclampsia (p < .0001), and Preeclampsia and eclampsia superimposed on preexisting HTN (p < .0001). In addition, in women with DM, the presence of diabetic retinopathy increased the risk of Preterm delivery (p = .002), cesarean section (p < .0001), requiring transfusion (p < .0001), and undergoing hysterectomy (p = .001), and were less likely to have a spontaneous vaginal delivery (p < .0001). However, the presence of diabetic retinopathy in women with DM did not increase the risk of the fetus being small at delivery, having intrauterine fetal demise, or congenital anomalies.Conclusion Women with diabetic retinopathy should be counseled about their increased risk of pregnancy-induced HTN, preeclampsia, premature delivery, cesarean section, transfusion, and hysterectomy.
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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 | 0.003 |
| 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 | 0.000 |
| 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".