August 2020 at a Glance: Focus on Neurohormonal Antagonists and Electrolytes
Bibliographic record
Abstract
Heart failure (HF) has a worldwide diffusion with high mortality and morbidity.[1][2][3] Groenewegen et al. 4 provide an updated overview of the epidemiology of HF. PathophysiologyMicroRNAs (miRNAs) are small non-coding RNAs potentially useful for the diagnosis and prognosis of HF. [5][6][7] They may also be useful for better understanding HF pathophysiology.Garg et al. 8 investigated the mechanisms of aldosterone-induced cardiac remodelling.After screening 2555 miRNAs, miR-181 was found to be a potential regulator of the aldosterone-mineralocorticoid receptor pathway with potential therapeutic implications. Electrolyte abnormalities PotassiumCooper et al. 9 showed a U-shaped association between serum potassium levels and mortality risk in 13 015 patients with HF and reduced ejection fraction (HFrEF) from the Swedish HF Registry.A potassium value of 4.2 mmol/L was associated with the lowest risk of death at 30 days, 12 months, and at long term.Hyperkalaemia is a major cause of underuse of reninangiotensin-aldosterone system inhibitors (RAASi).10,11 An analysis from the European Society of Cardiology (ESC)-HF Association (HFA) EURObservational Registry Programme (EORP) HF Long-Term Registry confirmed that hyperkalaemia is associated with mineralocorticoid receptor antagonist (MRA) non-prescription or discontinuation and poorer survival.At multivariable analyses, adjusting for RAASi discontinuation, RAASi discontinuation itself, but not hyperkalaemia, was an independent predictor of poorer outcomes.This suggests that the relation with outcomes of hyperkalaemia is mediated by RASSi discontinuation.12 Rossignol et al. 13 developed updated risk models for prediction of cardiovascular death, HF hospitalizations and all-cause death in HFrEF patients.In addition to traditional clinical variables, these models included time-dependent variables such as serum potassium, glomerular filtration rate and anaemia, as well as treatment with diuretics, MRA and beta-blockers.The addition of . . . . . . . . . . . . . . . . . . . . . . . . .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.422 | 0.248 |
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".