What Is the Risk of Developing a Severe Form of COVID-19 Infection Among Adults Who Currently Smoke Compared to Ex-smokers? Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Smoking is associated with an increased risk of chronic diseases and poorer outcomes in patients with COVID-19. Although lung function improves after smoking cessation, evidence directly comparing COVID-19 severity between current and former smokers remains limited. Objective: This systematic review aims to synthesize available evidence on the risk of developing severe COVID-19 among current smokers compared with former smokers. Methods and Analysis: This review will follow the PRISMA-P 2015 guidelines. Non-randomized studies published from December 2019 onward will be systematically searched in PubMed, Cochrane CENTRAL, Embase, and Epistemonikos. Additional studies will be identified through grey literature sources, relevant journals, and reference list screening. Eligible studies must report outcomes for both current and former smokers and include at least one marker of severe COVID-19: ICU admission, assisted ventilation, or death. Two reviewers will independently screen studies and extract data. Risk of bias will be assessed using the Newcastle–Ottawa Scale, and the GRADE framework will be applied to evaluate the certainty of the evidence. Statistical analyses will be conducted using R version 4.3.2. Heterogeneity will be quantified using the I² statistic; a fixed-effect model will be used when heterogeneity is low, and a random-effects model will be applied otherwise. As this review will use published data, ethical approval is not required. PROSPERO Registration Number: CRD42022368552.
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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 | 0.073 | 0.093 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.030 | 0.046 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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".