Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has reached a pandemic level. Very recently, I reported a significantly higher prevalence of diabetes and hypertension in severe COVID-19 as compared with non-severe COVID-19 by the meta-analysis. Considering that both diabetes and hypertension are risk factors for atherosclerosis, I further studied the prevalence of cardiovascular disease (CVD) in COVID-19 and found a significantly higher prevalence of CVD in severe patients than in non-severe patients. I speculate that the pre-existing vascular damage is associated with severity of COVID-19. A recent study showed that obese patients with COVID-19, despite their younger age, required more frequently assisted ventilation and access to intensive care units than normal weight patients. I thought that if the reason that COVID-19 is likely to become severe in obese people could be elucidated, the mechanism for aggravation of COVID-19 would be understood. As a result of considering a model of aggravation in obese people, I came up with the notion that pre-existing risk factors in obese people such as their vascular high-affinity for SARS-CoV-2, pro-inflammatory and pro-coagulant state and endothelial dysfunction may be likely to induce the development of “systemic severe coagulopathic vasculitis (SSCV)” in obese people. I believe that SSCV may largely contribute to the development of severe COVID-19. Here, I will describe the points of action of drugs for treating COVID-19 by using the SSCV model. J Endocrinol Metab. 2020;10(3-4):57-59 doi: https://doi.org/10.14740/jem659
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".