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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".