Why Female Smokers Have Poorer Long-Term Health Outcomes than Male Smokers: The Role of Cigarette Smoking During Pregnancy
Bibliographic record
Abstract
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 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".