Bibliographic record
Abstract
ince the first report of COVID-19 from Wuhan, China, the pandemic's infectivity and diverse outcomes have stunned the world.A consistent feature of COVID-19 is its predilection of inflicting adverse outcomes in patients with cardiovascular disease or risk factors for cardiovascular disease (1).Although many factors contribute to this association, a major mechanistic underpinning is the fact that the novel SARS-Cov-2 virus uses the transmembrane enzyme angiotensin-converting enzyme 2 (ACE2) as its key internalizing receptor (2).ACE2 has a key salutary function in the reninangiotensin system (RAS) by converting the pro-inflammatory vasoconstrictive octapeptide angiotensin II (1À8), to anti-inflammatory vasodilatory angiotensin (1À7).This has raised several key questions about COVID-19, along with controversy.First, does the increase ACE2 expression in cardiovascular diseases contribute to the worse outcomes of COVID-19 in cardiovascular patients?Second, is the ACE2 expression affected by cardiovascular medications?(There is a raging controversy that involves the use of RAS inhibitors.)Third, does the SARS-CoV-2
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.082 | 0.049 |
| Insufficient payload (model declined to judge) | 0.024 | 0.021 |
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".