Vasoactive exposures, vascular events, and hemifacial microsomia
Bibliographic record
Abstract
BACKGROUND: Based on experimental evidence and clinical observations, hemifacial microsomia (HFM) is one of several structural anomalies that are postulated to result from vascular disruption. We collected data in a case-control study to identify whether vasoactive exposures or vascular events during early pregnancy affect the risk of HFM. METHODS: Cases with a diagnosis of HFM were identified at craniofacial centers in 26 cities across the United States and Canada, from 1996 to 2002. Controls were matched to cases by age and pediatrician practice. Mothers of 230 cases and 678 controls were interviewed about pregnancy events and exposures. Case and control mothers were compared for early pregnancy use of vasoactive medications, cigarettes, and alcohol; singleton or multiple gestation; and diabetes, hypertension, or vaginal bleeding in the first half of pregnancy. RESULTS: Odds ratios (ORs) were significantly increased for vasoactive mediation use (OR, 1.9 overall; OR, 4.2 among smokers), multiple gestations (OR, 10.5), and diabetes (OR, 6.0). Vaginal bleeding in the second trimester and heavy alcohol intake were associated with increased risks, but the estimates were based on small numbers and, therefore, are unstable. No associations were observed for cigarette smoking without vasoactive medication use, hypertension, and vaginal bleeding in the first trimester. CONCLUSIONS: The increased risks of HFM associated with vasoactive medication use, multiple gestations, diabetes, and second trimester vaginal bleeding appear collectively to support the hypothesis that vascular disruption is one etiology for HFM, because each of these factors is related to effects on blood vessels.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".