Reply to: “Current smoking is not associated with COVID-19”
Bibliographic record
Abstract
Like M. Rossato and co-workers, we too have been struck by the relative underrepresentation of current smokers in cohorts of coronavirus disease 2019 (COVID-19) patients, particularly in light of our recent findings that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor angiotensin-converting enzyme II (ACE-2) is upregulated in the airway epithelium of this population [1]. China [2], Italy (as reported by M. Rossato and co-workers), and now New York City [3] have all reported current smoking rates below those of their respective general populations. The reason for this is a mystery. One possible explanation is misclassification of smoking status owing to under-reporting of smoking in these cohorts. Another is that smokers may be taking medications that may offer some protection against COVID-19 ( e.g. certain inhalers). Smoking and COPD are risk factors for severe 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.030 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".