May 2019 at a Glance: Epidemiology, Drug Effects On Biomarkers, Adverse Events With LVAD
Bibliographic record
Abstract
CardiomyopathyTreatment of cardiomyopathies is continuously evolving.1,2 All the aspects of heart failure (HF) diagnosis and treatment in different cardiomyopathies are covered in a position statement in this issue of the Journal.3 Epidemiology Heart failure in different continentsGeographical differences may be associated with different patient characteristics and outcomes.4 -6 Dewan et al. 7 compared the patients enrolled in Asia and in Western countries in two large trials with similar design.The analysis included 13 174 patients with HF and reduced ejection fraction (HFrEF).Compared with patients in Western Europe and America, Asian patients were younger and less likely to be on diuretics and devices.The rate of cardiovascular death/HF hospitalization was higher in Asia (e.g.Taiwan 17.2,China 14.9 per 100 patient-years) than in Western Europe (10.4) and North America (12.8).The adjusted risk of cardiovascular death was higher in many Asian countries than in Western Europe, except Japan, and the risk of HF hospitalization was lower in India and in the Philippines, but significantly higher in China, Japan, and Taiwan.7 Spot urinary sodium measurements can predict the response to diuretic therapy.13 -15 Biegus et al. 14 related spot urinary sodium measurements during the first 48 h of acute HF treatment with indices of decongestion, renal function, and prognosis.Overall, spot urinary sodium increased from baseline in the sample taken after
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.097 | 0.016 |
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".