Relationship Between Smoking and Pressure Injury Risk: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Smoking is a risk factor for many diseases. PURPOSE: This study explored the relationship between current or past smoking and pressure injury (PI) risk through a systematic review and meta-analysis. METHODS: The databases PubMed, Web of Science, and China National Knowledge Infrastructure were searched for the years between 2001 and 2020. Quality of evidence was estimated by the Newcastle-Ottawa Scale. The random effects model was applied to assess the odds ratios (OR) and 95% confidence intervals (CI); pooled adjusted OR and 95% CI, subgroup analysis, publication bias, sensitivity analyses, and meta-regression analysis were performed. RESULTS: Fifteen (15) studies (12 retrospective and 3 prospective) comprising data on 11 304 patients were eligible for inclusion in the review. The meta-analysis demonstrated that smoking increased the risk of PI (OR = 1.498; 95% CI, 1.058-2.122), and the pooled adjusted OR (1.969) and 95% CI (1.406-2.757) confirmed this finding. Publication bias was not detected by funnel plot, Begg's test (P = .322), or Egger's test (P = .666). Subgroup analyses yielded the same observations in both retrospective (OR = 1.607; 95% CI, 1.043-2.475) and prospective (OR = 1.218; 95% CI, 0.735-2.017) studies. The results were consistent across sensitivity analyses (OR = 1.07; 95% CI, 1.043- 2.475). Relevant heterogeneity moderators were not identified by meta-regression analysis with PI incidence (P = .466), years of patient data included (P = .637), mean patient age (P = .650), and diabetes mellitus diagnosis (P = .509). CONCLUSION: This study found that individuals who are current or formers smokers have an almost 1.5 times higher risk of PI development than do those who do not smoke.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".