The impact of smoking on COVID-19 morbidity and mortality
Bibliographic record
Abstract
Many articles by foreign authors, published in scientific journals with a stable international reputation, contain claims that smoking tobacco reduces the likelihood of infection with SARS-CoV-2. To study this issue, a correlation analysis was carried out to assess the dependence between the proportion of women and men who smoke in 94 countries located in Eurasia, North and South America, Australia, where more than 64 % of the world’s population lives, and the incidence and mortality of the population from COVID-19 during the period from February 1 to November 21, 2021. The results showed that an increase in the proportion of the population who smokes is always accompanied by an increase in morbidity and mortality among the world’s population. This tendency is especially pronounced in Europe, the USA and Canada, with the most detrimental effect of smoking on the growth of mortality. The results obtained allow us to reject with a high degree of confidence the conclusions about the protective effect of smoking from infection with SARS-CoV-2 and provide the media, medical, educational and educational institutions with additional arguments for informing the population about the negative consequences of smoking, especially during the COVID-19 pandemic.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".