Coronavirus disease 2019 ( <scp>COVID</scp> ‐19): angiotensin‐converting enzyme inhibitors, angiotensin <scp>II</scp> receptor blockers and cardiovascular disease
Bibliographic record
Abstract
As the world watches the spread of the coronavirus disease 2019 (COVID‐19) pandemic, affecting the health of millions of people and the lives of everyone, common health conditions including heart disease, stroke, cancer and other chronic diseases continue. While there is no doubt that there are direct consequences for morbidity and mortality of COVID‐19, including its direct cardiovascular effects, it will be important to ensure that these are not matched by the indirect consequences. Countries are at different stages in the natural history of the pandemic, but there is a clear pattern. Overloaded health systems necessitate the hasty development of new protocols and pathways for common conditions that deviate from established guidelines and that may be caused by changes in community behaviour, either imposed or arising from fear. Unproven therapies are being tested in the field and, in the absence of evidence, there is the potential for theory to drive practice to an extent that is generally not seen in conditions with an established evidence base.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".