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 <https://bit.ly/2yWp3jb>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".