Smoking during pregnancy and vision difficulties in children: a systematic review
Bibliographic record
Abstract
Cigarette smoking during pregnancy is a major public health concern. Intra-uterine exposure to maternal cigarette smoking is associated with increased risks of growth and neurodevelopmental problems during childhood and later life. Few studies have focussed on visual difficulties in children in the context of maternal smoking during pregnancy. A systematic search of online databases was carried out between February and May 2013 to examine the trend in visual outcomes in children exposed to maternal cigarette smoking during intra-uterine life. Twenty-four non-randomized studies were identified. Each study was rated for quality using the Newcastle-Ottawa Scale. Most studies (n = 18) reported fetal exposure to active or passive maternal cigarette smoking to be associated with an increased risk of adverse visual outcomes in children. In particular, there were higher rates of strabismus, refractive errors and retinopathy among children of women who smoked during pregnancy. These findings suggest that fetal exposure to cigarette smoke is a significant risk factor for visual problems during later life and that certain visual faculties, such as the intraocular muscles and retinal neurons, are more affected than others. The findings provide evidence in support of public health policies aimed at reducing fetal exposure to smoking by advising both women and their partners to quit smoking during pregnancy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".