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Record W4392285616 · doi:10.3389/phrs.2024.1605579

Why Female Smokers Have Poorer Long-Term Health Outcomes than Male Smokers: The Role of Cigarette Smoking During Pregnancy

2024· article· en· W4392285616 on OpenAlexaff
Yang Li, Yunchun Zhou, Mingyan Jiang, Wendy Wen, Yanfang Guo, Smita Pakhalé, Shi Wu Wen

Bibliographic record

VenuePublic health reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsPublic Health OntarioUniversity of OttawaHealth CanadaChildren's Hospital of Eastern OntarioOttawa Hospital
Fundersnot available
KeywordsPregnancyMedicineCigarette smokingMEDLINEYoung adultObstetricsGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Objectives: Women’s health status is better than men but the opposite is true for female smokers who usually have poorer long-health outcomes than male smokers. The objectives of this study were to thoroughly reviewed and analyzed relevant literature and to propose a hypothesis that may explain this paradox phenomenon. Methods: We conducted a search of literature from three English databases (EMBASE, MEDLINE, and Google Scholar) from inception to 13 November 2023. A combination of key words and/or subject headings in English was applied, including relevant terms for cigarette smoking, sex/gender, pregnancy, and health indicators. We then performed analysis of the searched literature. Results: Based on this review/analysis of literature, we proposed a hypothesis that may explain this paradox phenomenon: female smokers have worse long-term health outcomes than male smokers because some of them smoke during pregnancy, and the adverse effects of cigarette smoking during pregnancy is much stronger than cigarette smoking during non-pregnancy periods. Conclusion: Approval of our pregnancy-amplification theory could provide additional evidence on the adverse effect on women’s long-term health outcomes for cigarette 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.096
GPT teacher head0.377
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2024
Admission routes1
Has abstractyes

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