February 2018 at a Glance: Heart and Brain Interaction, Prognostic Variables, and Acute Heart Failure and Post-Discharge Outcomes
Bibliographic record
Abstract
Heart and brainA position paper from the Heart Failure Association (HFA) reviews the heart and brain interactions in patients with heart failure (HF). 1 These include cerebral hypoperfusion, causing either ischaemic stroke, symptomatic or not, or a progressive decline in brain function, 2,3 abnormalities in cortical functions, with cognitive decline, dementia, depression and anxiety, abnormalities in the autonomic nervous system and cardiac reflexes, changes related to HF treatment.A final section of this fascinating review covers the existing gaps in knowledge and unresolved issues.1 Prognostic variablesExpanding demographic variables: the role of employment status Rørth et al. 4 examined the association between employment status and the risk for all-cause mortality and recurrent HF hospitalization in a nationwide Danish cohort of 25 571 patients hospitalized for HF in the years 1997-2015.Not being part of the workforce at the time of the initial hospitalization was associated with a significantly higher risk of death [hazard ratio (HR) 1.59; 95% confidence interval (CI) 1.50-1.68]and rehospitalization for HF in analyses adjusted for the other demographic factors.4
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.017 |
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".