Adequacy of Ambulatory Hemodynamic Assessments for Reducing All-Cause Mortality in Individuals With Heart Failure
Bibliographic record
Abstract
associated with significant reduction of cardiac output has evolved to include conditions such those of moderate and preserved ejection fraction. While the prevalence of HF in the population is increasing, it is not HF with reduced ejection fraction that is driving the trajectory upward for mortality. There is some evidence to suggest that a better understanding of the pathophysiology, novel pharmacological strategies, devices, as well as remote monitoring of the hemodynamics seem to account for a reduction in the cardiovascular mortality and re-hospitalization in some cohorts with HF. However, the all-cause mortality associated with HF has not been reduced significantly by the current interventions. To explore the potential approaches needed for the strategies and avenues to reduce all-cause mortality in patients with HF, it would be helpful to evaluate the evidence in the literature directed at the care of patients with chronic/acute decompensated HF. It is evident that ambulatory measurements of pressures and volume are pivotal in a better management of HF but unless the interventions extend to an improvement in the renal function, the chances of reducing all-cause mortality seems modest. Therefore, future directions of interventions must not only be directed at close monitoring of pressures and volume simultaneously in HF patients but also at improving renal function. Moreover, it is clear that venous congestion plays a detrimental role in the deterioration of renal function and until measures are in place to reduce it, all-cause mortality will not decrease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".