A longitudinal study of changes in depressive symptoms and risk factors for congestive heart failure
Bibliographic record
Abstract
BACKGROUND: Depression is prevalent among patients with congestive heart failure (CHF) and is associated with increased mortality and healthcare use. However, most research on this association has focused on high-income countries, leaving a gap in knowledge regarding the relationship between depression and CHF in low-to-middle-income countries. AIMS: To identify changes in depressive symptoms and potential risk factors for poor outcomes among CHF patients. METHODS: Longitudinal data from 783 patients with CHF from public hospitals in Karachi, Pakistan, were analysed. Depressive symptom severity was assessed using the Beck Depression Inventory. Baseline and 6-month follow-up Beck Depression Inventory scores were clustered using Gaussian mixture modelling to identify separate depressive symptom subgroups and extract trajectory labels. Further, a random forest algorithm was used to determine baseline demographic, clinical and behavioural predictors for each trajectory. RESULTS: Four separate patterns of depressive symptom changes were identified: 'good prognosis', 'remitting course', 'clinical worsening' and 'persistent course'. Key factors related to these classifications included behavioural and functional factors such as quality of life and disability, as well as the clinical severity of CHF. Specifically, poorer quality of life and New York Heart Association (NYHA) class 3 symptoms were linked to persistent depressive symptoms, whereas patients with less disability and without NYHA class 3 symptoms were more likely to exhibit a good prognosis. CONCLUSIONS: By examining the progression of depressive symptoms, clinicians can better understand the factors influencing symptom development in patients with CHF and identify those who may require closer monitoring and appropriate follow-up care.
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) | 0.000 | 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 | 0.000 |
| 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".