Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aims The associations between potassium level and outcomes, the effect of sacubitril–valsartan on potassium level, and whether potassium level modified the effect of sacubitril–valsartan in patients with heart failure and a reduced ejection fraction were studied in PARADIGM-HF. Several outcomes, including cardiovascular death, sudden death, pump failure death, non-cardiovascular death and heart failure hospitalization, were examined. Methods and results A total of 8399 patients were randomized to either enalapril or sacubitril–valsartan. Potassium level at randomization and follow-up was examined as a continuous and categorical variable (≤3.5, 3.6–4.0, 4.1–4.9, 5.0–5.4 and ≥5.5 mmol/L) in various statistical models. Hyperkalaemia was defined as K+ ≥5.5 mmol/L and hypokalaemia as K+ ≤3.5 mmol/L. Compared with potassium 4.1–4.9 mmol/L, both hypokalaemia [hazard ratio (HR) 2.40, 95% confidence interval (CI) 1.84–3.14] and hyperkalaemia (HR 1.42, 95% CI 1.10–1.83) were associated with a higher risk for cardiovascular death. However, potassium abnormalities were similarly associated with sudden death and pump failure death, as well as non-cardiovascular death and heart failure hospitalization. Sacubitril–valsartan had no effect on potassium overall. The benefit of sacubitril–valsartan over enalapril was consistent across the range of baseline potassium levels. Conclusions Although both higher and lower potassium levels were independent predictors of cardiovascular death, potassium abnormalities may mainly be markers rather than mediators of risk for death.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it