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 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".