Association of Smoking Habits of Mother during Pregnancy with Pregnancy Outcome
Bibliographic record
Abstract
Background: The objectives of this work were to study the association of maternal smoking habits with stillbirths, abortions, neonatal deaths, birth weights, placental weights and the outcomes on the 28th day of life.Methods: A questionnaire was developed and completed with the hospitals' recorded data collected over a period of 5 years from 47,000 babies born in several hospitals in Ontario, Canada. The mothers were classified into four categories: non-smokers, light smokers (less than 10 cigarettes per day), moderate smokers (between 10 and 19 cigarettes per day) and heavy smokers (20 or more cigarettes per day). The population surveyed was of mixed ethnicity from both rural and urban areas. Statistical analysis was performed using the SPSS statistical package.Results: Even the light smoking habit has an effect on the birth weight and the placental weight but for other characteristics, stillbirth, abortion, and the outcomes on the 28th day of life, no significant difference observed between light smokers and non-smokers.Conclusion: While quit smoking must be the ultimate goal for any smoker, the present study concludes that moderate and heavy smokers, if they will not be able to quit, they should reduce their number of cigarettes per day to at least the level of light smokers to achieve the same results for non-smokers. All characteristics show significant difference between non-smokers and moderate and heavy smokers.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".