A meta‐analysis study on the association between smoking and male pattern hair loss
Bibliographic record
Abstract
BACKGROUND: Smoking-which often refers to recreational consumption of the nicotine-containing tobacco-is deemed a risk factor for both the development of and worsening of androgenetic alopecia (AGA). However, there is no published meta-analysis study on the effect of smoking on AGA; so, we quantitatively synthesized the evidence base pertaining to the recreational activity and this form of hair loss in men. METHODS: We systematically searched PubMed and Scopus to identify published studies with suitable data, and pairwise meta-analyses were conducted. RESULTS: Our search identified eight studies-and the data thereof were used across four meta-analyses. We found that ever smokers are significantly (p < 0.05) more likely, than never smokers, to develop AGA (pooled odds ratio (OR) = 1.82, 95% confidence interval (CI): 1.55-2.14). Our results showed that the odds of developing AGA are significantly (p < 0.05) higher in men who smoke at least 10 cigarettes per day, than in their counterparts who smoke up to 10 cigarettes per day (pooled OR = 1.96, 95% CI: 1.17-3.29). For men with AGA, the odds of disease progression are significantly (p < 0.05) higher among ever smokers than in never smokers (pooled OR = 1.27, 95% CI: 1.01-1.60). We found no significant (p ≥ 0.05) association between smoking intensity and disease progression. CONCLUSIONS: Findings from the current study-which is the first meta-analysis to our knowledge reviewing the association between AGA and the extent of smoking, can guide further research and update clinical practice guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".