March 2016 at a Glance. Focus on Right Ventricular Failure and Acute Heart Failure
Bibliographic record
Abstract
The right ventricle has been always neglected as a cause of heart failure (HF). It is difficult to study with imaging techniques and, even more, difficult to treat when failing. The statement in this issue of the journal about the management of acute right ventricular failure is therefore particularly welcome.1 All the aspects regarding the pathophysiology, assessment and treatment of this condition are outlined. With respect to epidemiology, Van Riet et al. have reviewed the studies reporting the prevalence of HF and left ventricular (LV) dysfunction in community-dwelling people. These were of 5.5% and 36.0%, respectively.2 A study of 37,308 Swedish men shows that adherence to a Mediterranean diet is associated with a lower risk of HF and of HF mortality, confirming and extending previous data.3-5 Cancer is an important comorbidity in HF.6 A long-term follow-up study in Denmark shows that the incidence of cancer is also increased by 24% in the patients with HF, compared with the subjects without HF, and patients with HF and cancer had the worst outcomes.7 With respect to prognosis, Demissei et al. have assessed the prognostic value of 48 biomarkers measured in the PROTECT trial. Forty-four biomarkers were associated with outcomes, but 42 had limited value. Multimarker models with blood urea nitrogen (BUN), chloride, IL-6, cTnI, sST-2 and VEGFR-1 had a much stronger prognostic value. With the exception of BUN and galectin-3, late measurements provided superior accuracy for 180 days mortality.8 The independent prognostic value of plasma endothelin-1 in patients with acute HF is shown in the ASCEND-HF database.9 Body mass index had a U-shaped relation with in-hospital mortality in the patients with acute HF in the ALARM-HF study but this relation was lost after adjustment for covariates such as comorbidities and HF treatment.10 The long-term effects of treatment with Algysil intramyocardial injections in patients with HF in the AUGMENT-HF trial are shown with a long-term improvement in all the parameters related with exercise capacity and further data regarding cardiac remodelling and events.11
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.177 | 0.140 |
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".