Bibliographic record
Abstract
Alaa Badawi1,2 1Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto, ON, Canada; 2Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, CanadaCorrespondence: Alaa BadawiPublic Health Risk Sciences Division, Public Health Agency of Canada, 180 Queen Street West, Toronto, ON M5V 3L7, CanadaEmail alaa.badawi@canada.caAbstract: Severe acute respiratory syndrome (SARS) coronavirus (CoV)-2 (SARS-CoV-2) is a novel coronavirus identified as the cause of coronavirus disease-2019 (COVID-19) that began in Wuhan, China in late 2019 and spread now in 210 countries and territories around the world. Many people are asymptomatic or with mild symptoms. However, in some cases (usually the elderly and those with comorbidities) the disease may progress to pneumonia, acute respiratory distress syndrome and multi-organ dysfunction that can lead to death. Such wide interindividual differences in response to SARS-CoV-2 infection may relate to several pathogen- and host-related factors. These include the different levels of the ubiquitously present human angiotensin I converting enzyme 2 (ACE2) receptors gene expression and its variant alleles, the different binding affinities of ACE2 to the virus spike (S) protein given its L- and S-subtypes and the subsequent extent of innate immunity-related hypercytokinemia. The extensive synthesis of cytokines and chemokines in coronavirus diseases was suggested as a major factor in exacerbating lung damage and other fatal complications. The polymorphisms in genes coding for pro-inflammatory cytokines and chemokines have been associated with mediating the response and susceptibility to a wide range of infections and their severe outcomes. Understanding the nature of pathogen–host interaction in COVID-19 symptomatology together with the role of hypercytokinemia in disease severity may permit developing new avenues of approach for prevention and treatment and can delineate public health measures to control the spread of the disease.Keywords: SARS-CoV, MERS-CoV, SARS-CoV-2, COVID-19, hypercytokinemia
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".