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Record W2031182582 · doi:10.1111/aos.12627

Smoking during pregnancy and vision difficulties in children: a systematic review

2014· review· en· W2031182582 on OpenAlexaboutno aff
Michelle Fernandes, Xiao Yang, Jinying Y. Li, Leila Cheikh Ismail

Bibliographic record

VenueActa Ophthalmologica · 2014
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyMedicineContext (archaeology)StrabismusRetinopathy of prematurityPublic healthObstetricsEnvironmental healthPediatricsGestational ageOphthalmologyNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.355
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations34
Published2014
Admission routes1
Has abstractyes

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