Smoking Is Independently Associated With an Increased Risk for COVID-19 Mortality: A Systematic Review and Meta-analysis Based on Adjusted Effect Estimates
Bibliographic record
Abstract
INTRODUCTION: Smoking can cause mucociliary clearing dysfunction and poor pulmonary immunity, leading to more severe infection. We performed this study to explore the association between smoking and mortality of coronavirus disease 2019 (COVID-19) patients utilizing a quantitative meta-analysis on the basis of adjusted effect estimates. AIMS AND METHODS: We conducted a systematic search of the online databases including PubMed, Web of Science, Scopus, and Embase. Only articles reporting adjusted effect estimates on the association between smoking and the risk of mortality among COVID-19 patients in English were included. Newcastle-Ottawa scale was fitted to assess the risk of bias. A random-effects model was applied to calculate the pooled effect with the corresponding 95% confidence interval (CI). RESULTS: A total of 73 articles with 863 313 COVID-19 patients were included in this meta-analysis. Our results indicated that smoking was significantly associated with an increased risk for death in patients with COVID-19 (pooled relative risk = 1.19, 95% CI = 1.12-1.27). Sensitivity analysis indicated that our results were stable and robust. CONCLUSIONS: Smoking was independently associated with an increased risk for mortality in COVID-19 patients. IMPLICATIONS: This present study may contribute to summarizing the association between smoking and the risk of COVID-19 mortality based on adjusted effect estimates. More detailed and complete data on smoking status should be collected to more accurately estimate the effect of smoking on COVID-19 mortality.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".