Additional file 1 of Comparison of renin–angiotensin–aldosterone system inhibitors with other antihypertensives in association with coronavirus disease-19 clinical outcomes
Bibliographic record
Abstract
Additional file 1: Table S1. PRISMA Checklist. Table S2. Quality score of articles (Newcastle–Ottawa Scale). Figure S1. Risk of poor COVID-19 clinical outcome with ACEIs relative to ARBs. Figure S2. Risk of poor COVID-19 clinical outcome with ACEIs relative to BBs. Figure S3. Risk of poor COVID-19 clinical outcome with ACEIs relative to CCBs. Figure S4. Risk of poor COVID-19 clinical outcome with ACEIs relative to thiazides. Figure S5. Risk of poor COVID-19 clinical outcome with ACEIs relative to all other antihypertensives. Figure S6. Risk of poor COVID-19 clinical outcome with ARBs relative to all other antihypertensives. Figure S7. Risk of poor COVID-19 clinical outcome with ARBs relative to BBs. Figure S8. Risk of poor COVID-19 clinical outcome with ARBs relative to CCBs. Figure S9. Risk of poor COVID-19 clinical outcome with ARBs relative to thiazides. Figure S10. Risk of poor COVID-19 clinical outcome with ARBs relative to all other non-RAAS antihypertensives. Figure S11. Risk of poor COVID-19 clinical outcome with ACEIs relative to all other non-RAAS antihypertensives. Figure S12. Risk of poor COVID-19 clinical outcome with CCBs relative to ACEI, ARBs, BBs. Figure S13. Risk of poor COVID-19 clinical outcome with ACEI, ARBs, BBs relative to CCBs and thiazides.
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 | 0.003 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.801 | 0.055 |
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".