Editorial: Methods and application in cardiovascular and smooth muscle pharmacology: 2021
Bibliographic record
Abstract
Despite significant advances in basic, translational, and clinical research tackling heart disease, cardiovascular pathologies remain among the leading causes of mortality and morbidity worldwide, being responsible for one-third of global deaths as estimated by the WHO (Organization, 2021). The complexity of risk factors and pathways underlying the development of cardiovascular disorders (CVDs) limits the efficacy of a given therapeutic intervention and necessitates combined pharmacological approaches, as well as lifestyle modification to provide a reasonable health impact (Arnett et al., 2019). Be that as it may, there remains a considerable room for scientific inquiry in pursuit of novel and more refined avenues to prevent, diagnose, mitigate, and reverse different forms of cardiovascular ailment, as well as optimize patient management. Indeed, such a need for research in this field was even further emphasized as the world faced heightened health challenges during the COVID-19 pandemic with cardiovascular complications being among the most serious consequences of SARS-CoV-2 infection (Wehbe et al., 2020).
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".