The relation of passive smoking with cervical cancer
Bibliographic record
Abstract
BACKGROUND: Published studies about passive smoking and cervical cancer have found inconsistent results. Hence, the present meta-analysis was performed to assess this association. METHODS: A systematical search was performed to identify eligible cohort and case-control studies in PubMed, Scopus, Elsevier ScienceDirect, and Web of Science databases (up to March, 2018). The quality of included studies was assessed by the Newcastle-Ottawa quality scale (NOS). The random effects model (REM) was used to calculate the pooled odds ratio (ORs). Subgroup and sensitivity analyses were performed. Publication bias was assessed by funnel plot, using Begg's test and Egger's test. RESULTS: Around 14 eligible studies were included for analysis, which included a total of 384,995 participants. The pooled ORs of passive smoking with cervical cancer risk was 1.70 (95% CI: 1.40-2.07, I = 64.3%). Subgroups stratified by continent, study design, quality score, and cervical cancer types/phases suggested that the result was robust. For instance, the pooled ORs for the cohort and case-control studies was 1.37 (95% CI: 1.16-1.62, I = 0%) and 2.09 (95% CI: 1.52-2.89, I = 76.6%), respectively. The pooled ORs ranged from 1.61 (95%CI: 1.34-1.92) to 1.77 (95%CI: 1.44-2.16) after one study was removed each time in the sensitivity analyses, indicating that the result was stable. Publication bias was detected by funnel plot and Egger's tests. The recalculated ORs were 1.33 (95% CI: 1.21-1.47). CONCLUSIONS: This meta-analysis provides evidence that passive smoking is associated with an increased risk of cervical cancer.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".