Perception of Teratogenic Risk and the Rated Likelihood of Pregnancy Termination: Association with Maternal Depression
Bibliographic record
Abstract
OBJECTIVE: Women are often exposed to various medications and medical conditions during pregnancy. Unrealistically high maternal teratogenic risk perception, related to these exposures, may lead to abrupt discontinuation of therapy and (or) termination of a wanted pregnancy. The association between maternal depression and the teratogenic risk perception has not been studied, nor were the actions resulting from this perception. Our objectives were to explore the association between maternal depression, teratogenic risk perception, and the rated likelihood to terminate pregnancy. Additionally, we evaluated possible benefits of counselling. METHODS: We administered the Edinburgh Postnatal Depression Scale (EPDS) to all women who attended the Motherisk Clinic between October 2007 and April 2010. A visual analogue scale was used to determine maternal risk perception in relation to the specific exposure, and the rated likelihood to terminate the pregnancy, before and after counselling. RESULTS: We analyzed data from 413 women. Maternal teratogenic risk perception and the rated likelihood to terminate the pregnancy were significantly lower following counselling. An EPDS score of 13 or more was significantly associated with a higher rated likelihood to terminate the pregnancy (P = 0.03). In a multivariable regression analysis, an EPDS score of 13 or more was found to be an independent predictor of a higher personal teratogenic risk perception (P = 0.03). CONCLUSIONS: Both maternal depression and exposure-directed counselling are associated with maternal risk perception and the rated likelihood to terminate pregnancy. Appropriate counselling may reduce fear of teratogenicity and the likelihood of pregnancy termination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".