Additional file 1 of Stopping renin-angiotensin system blockers after acute kidney injury and risk of adverse outcomes: parallel population-based cohort studies in English and Swedish routine care
Bibliographic record
Abstract
Additional file 1 : Figure S1. Study design diagram, English cohort. Table S1. Top 10 admission codes for baseline AKI admission in English cohort. Table S2. Baseline characteristics of English and Swedish cohorts (overall and heart failure outcome analysis). Figure S2. Represcribing ACEI/ARB in both cohorts. Figure S3. Represcribing by year in English cohort. Table S3. Baseline characteristics of people censored during immortal time, both cohorts. Table S4. Absolute rates and hazard ratios for all outcomes, both cohorts. Table S5. Model building for both cohorts. Figure S4., Table S6. Propensity score analysis. Table S7. Main and sensitivity analyses results, heart failure outcome. Table S8. Main and sensitivity analyses results, acute kidney injury outcome. Table S9. Main and sensitivity analyses, stroke outcome. Table S10. Main and sensitivity analyses, mortality outcome. Figure S5. Summary forest plot of all main and sensitivity analyses, all outcomes.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.760 | 0.058 |
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".