A New Look at the Neonate's Clinical Presentation After In Utero Exposure to Antidepressants in Late Pregnancy
Bibliographic record
Abstract
OBJECTIVES: To identify symptoms in neonates exposed to antidepressants in late pregnancy and to propose a categorization of these symptoms to help clinical assessment of antidepressant effects in exposed neonates. METHODS: Data were extracted retrospectively from maternal and neonatal hospital charts. A total of 73 neonates exposed to antidepressant and 73 nonexposed neonates were included. Neonatal symptoms reported in the literature to be related to antidepressant exposure were collected. Multiple logistic regression analysis was used to estimate the association of neonatal symptoms and antidepressant exposure. Factorial analysis was used to regroup the neonatal symptoms. RESULTS: Increased risk of alertness alteration (odds ratio [OR], 37;95% confidence interval [CI], 8-174), altered muscular tone (OR,20; 95% CI, 5-71), feeding and GI problems (OR, 3.8; 95% CI, 1.7-8.1), tachypnea (OR, 2.5; 95% CI, 1.1-5.3), and neurological problems (8/73 vs 0/73; P = 0.006) were found. Three statistically significant clusters of symptoms associated with antidepressant exposures emerged from the factorial analysis. Two of these clusters were similar to those described in adults for serotonergic toxicity and antidepressant discontinuation syndrome while the other was closely related to neonatal immaturity. CONCLUSIONS: Symptoms expressed in neonates exposed to antidepressants in late pregnancy could be gathered in three clusters. This grouping could be useful to develop a new tool helping in the assessment and care of the exposed newborns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".