Bibliographic record
Abstract
The three letters from D. Lutchman, K.D. McAlinden and co-workers, and K. Farsalinos and co-workers together capture the divergence in opinion on the impact of smoking on coronavirus disease 2019 (COVID-19) and whether the angiotensin-converting enzyme 2 (ACE-2) receptor mediates this relationship. At the heart of this controversy is whether smoking reduces or increases the risk of contracting COVID-19. K. Farsalinos and co-workers, through analysis of the pooled prevalence of current smoking across 11 case series determined that current smoking status was significantly lower than expected gender- and age-adjusted prevalence in COVID-19 patients. That smoking could potentially be protective against COVID-19 has not gone unnoticed by the public. Since late April, multiple media outlets have reported on this possibility, prompting the World Health Organization (WHO) to release a warning on 4 May, 2020, on tobacco use during this pandemic [1]. While we do not dispute that the prevalence of smoking in COVID-19 cases has been surprisingly low across the world, we would echo WHO's advice, based on emerging evidence that outcomes in COVID-19 are worse in patients who do smoke. An analysis conducted by Killerby et al. [2], of 220 hospitalised and 311 nonhospitalised patients with COVID-19 patients across six acute care hospitals and associated outpatient clinics in metropolitan Atlanta, Georgia, for instance, demonstrated that smoking was an independent risk factor for COVID-19 hospitalisation, carrying an odds ratio of 2.3 (95% CI 1.2–4.5). A recent meta-analysis has also shown that smokers have a relative risk of 1.34 (95% CI 1.07–1.67) of having more severe disease or experiencing refractory or progressive disease [3]. While smoking may not necessarily increase one's risk for contracting COVID-19, the biological and inflammatory cascade that occurs upon severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may be particularly devastating for a smoker. Smoking increases severity of COVID-19
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".