POSSIBLE CONFOUNDERS IN THE REPORTED ASSOCIATION BETWEEN AUTISM SPECTRUM DISORDER (ASD) AND EXPOSURE TO SELECTIVE SEROTONIN REUPTAKE INHIBITORS (SSRIS) DURING PREGNANCY
Bibliographic record
Abstract
Background Autism has been in the forefront of public concern because of reported increase in prevalence and growing interest in the role of environmental risk factors in autism. A recent meta-analysis by Man et al. (2015) reported an increased risk of ASD in children of mothers exposed to SSRIs during pregnancy (adjusted OR 1.81, 95% CI 1.47–2.24). However, association may not imply a causal relation between SSRI exposure and ASD. We hypothesize that underlying disease might have confounded the published result. Materials and methods A literature review was performed in order to identify possible confounders in the reported association. The list of search terms included but was not limited to following terms: ‘Pregnancy', ‘maternal', ‘depression', ‘child behaviour', ‘health care seeking behaviour'. Results Retrieved articles were classified in following four domains of possible confounders: 1/ direct link between depression and ASD, 2/effect of depression on interaction with the child, 3/effect of depression on other risk factors of ASD and 4/ascertainment bias. In the last domain, we examined the effect of depression on the way mothers perceive and report on the behaviour of their child and the effect of maternal depression on healthcare seeking behaviour. Analysis suggests that there are important merits to all those four domains. Conclusion Because of obvious ethical reasons, research on exposure during pregnancy is mostly restricted to systematic review and meta-analysis of observational studies. Although this has resulted in a treasury of information, possible confounders must be taken into account when interpreting the results.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".