Marijuana smoking and asthma: a protocol for a meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Recent studies have raised the concern on the risk of asthma in marijuana smokers; however, the results remain controversial and warrant further investigation. With a growing number of marijuana smokers, examining the association between marijuana smoking and asthma and quantifying such association through meta-analysis have important implications for public health and clinical decision-making. In view of this, the present protocol aims to detail a comprehensive plan of meta-analysis on the association aforementioned. The findings are expected to strengthen the current knowledge base pertaining to the potential adverse effects of marijuana smoking on pulmonary health and to facilitate the development of prevention strategies for asthma. METHODS AND ANALYSIS: The MEDLINE/PubMed, Web of Science and EMBASE databases will be searched systematically from inception to 1 September 2021 to retrieve the relevant observational studies focusing on the association between marijuana smoking and asthma. Both unadjusted and adjusted effect sizes, such as OR, relative risk, HR and the corresponding 95% CIs will be extracted for pooled analyses. Heterogeneity and publication bias across the included studies will be examined. The Newcastle-Ottawa Quality Scale will be used to assess the quality and risk of bias. Statistical software Review Manager V.5.3 and Stata V.11.0 will be used for statistical analyses. ETHICS AND DISSEMINATION: Since no private and confidential patient data will be included in the reporting, approval from an ethics committee is not required. The results will be published in a peer-reviewed journal or disseminated in the relevant conferences. The study raises no ethical issue. OSF REGISTRATION NUMBER: 10.17605/OSF.IO/UPTXC.
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 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 | 0.094 | 0.156 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.071 | 0.009 |
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".