Bibliographic record
Abstract
PURPOSE OF REVIEW: It is well known that patients with heart failure also suffer from a large number of comorbid conditions, which confound their heart failure management and adversely affect the prognosis. The purpose of this review is to evaluate the latest developments of these conditions. RECENT FINDINGS: Chronic lung disease commonly coexists with heart failure. It is more prevalent and worsens prognosis more in patients with preserved (heart failure with preserved ejection fraction) than with reduced ejection fraction (heart failure with reduced ejection fraction). Patients with diabetes have increased risk of incident heart failure, and as a comorbid condition it adversely affects prognosis. The relative impact on mortality and heart failure hospitalization remains controversial. Renal dysfunction is also common in patients with heart failure, with similar prevalence among those with preserved ejection fraction and those with reduced ejection fraction. The prognosis seems mainly related to long-term changes in kidney function, rather than to short-term changes in serum creatinine. Anemia and iron deficiency have a similar profile in terms of prevalence and impact on prognosis. Recent data suggest a benefit of intravenous iron infusion in patients who are iron deficient. SUMMARY: As patients with comorbid conditions are frequently excluded from clinical trials, future clinical trials should recruit these patients and include endpoints that will be reflective of these conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".