Comparing Obstetrical Outcomes Between Attention Deficit Hyperactivity Disorder and Attention Deficit Disorder: A Population-Based Studys
Bibliographic record
Abstract
Objectives: Attention deficit hyperactivity disorder (ADHD) is among the most common neurodevelopmental disorders affecting women of reproductive age. Previous data on this condition did not study its different symptom clusters separately. Our aim was to compare perinatal outcomes between women with hyperactivity cluster (ADHD) and those with the inattentive cluster (attention deficit disorder (ADD)). Methods: A retrospective population-based study utilizing data from the Healthcare Cost and Utilization Project–Nationwide Inpatient Sample (HCUP-NIS). All deliveries or maternal deaths from 2004 to 2014 were available for analysis, and perinatal outcomes were compared between participants with an ADD diagnosis and those with an ADHD diagnosis. A multivariate logistic regression was used to control for confounders. Results: During the study period, there were 9,096,788 deliveries. Of them, 7103 had an ADHD diagnosis, and 2928 had an ADD diagnosis. Women with ADHD, compared to those with ADD, were more likely to be younger than 25 years of age; to be Black; to be from a lower income quartile; to smoke tobacco during pregnancy; and to use illicit drugs (p < 0.001 for all). Using multivariate logistic regression, women with ADHD, compared to those with ADD, had a higher rate of hypertensive disorders of pregnancy (HDPs) (aOR 1.19, 95% CI 1.03–1.37, p = 0.02), preterm delivery (aOR 1.19, 95% CI 1.01–1.39, p = 0.038), maternal infection (aOR 1.39, 95% CI 1.04–1.85, p = 0.024), and small-for-gestational-age (SGA) neonates (aOR 1.33, 95% CI 1.04–1.69, p = 0.022). Conclusions: Women with an ADHD diagnosis, compared to those with ADD, had a higher incidence of various maternal and neonatal complications, including HDPs, preterm delivery, and SGA neonates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".