The effect of reproductive hormones on women’s daily smoking across the menstrual cycle
Bibliographic record
Abstract
BACKGROUND: Women attempt to quit smoking less often than men and are less likely to maintain abstinence. Reproductive hormones have been postulated as a reason for this sex difference, though this remains to be clarified. Research suggests that estradiol and progesterone may influence nicotine addiction, though various methodologies have led to inconsistent findings. The current study aimed to directly examine the effect of reproductive hormones on women's smoking behavior. METHODS: Over the course of one menstrual cycle, twenty-one female smokers recorded the number of cigarettes smoked in a day, as well as their perceived need for and enjoyment of cigarettes smoked. Additionally, they provided 12 urine samples for the measurement of the urinary metabolites of estradiol (estrone-3-glucuronide, E1G) and progesterone (pregnanediol glucuronide, PdG). Multilevel modeling was used to examine the effects of hormone levels as well as hormone change on smoking outcomes. RESULTS: When PdG levels were low, they were inversely associated with daily cigarettes smoked. Furthermore, E1G level was negatively associated with both self-reported need for and enjoyment of cigarettes smoked but not the number of cigarettes smoked. Examining the effect of hormonal change on smoking outcomes revealed a significant interaction between change in PdG and E1G on number of cigarettes smoked such that only a simultaneous drop or increase in both hormones was associated with a greater number of cigarettes. Hormonal change effects on need for and enjoyment of cigarettes were not significant. CONCLUSIONS: The present study suggests that (1) elevated progesterone levels lessen the propensity to smoke in women, (2) estrogen levels influence women's subjective experience of smoking, and (3) simultaneous drops or increases in these hormones are associated with increased smoking.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".