Up-Beat UK: A programme of research into the relationship between coronary heart disease and depression in primary care patients
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease and depression are both common health problems and by 2020 will be the two leading causes of disability worldwide. Depression has been found to be more common in patients with coronary heart disease but the nature of this relationship is uncertain. In the United Kingdom general practitioners are now being remunerated for case-finding for depression in patients with coronary heart disease, however it is unclear how general practitioners should manage these patients. We aim to explore the relationship between coronary heart disease and depression in a primary care population and to develop an intervention for patients with coronary heart disease and depression. METHODS/DESIGN: This programme of research will consist of 4 inter-related studies. A 4 year prospective cohort study of primary care patients with coronary heart disease will be conducted to explore the relationship between coronary heart disease and depression. Within this, a nested case-control biological study will investigate genetic and blood-biomarkers as predictors of depression in this sample. Two qualitative studies, one of patients' perspectives of treatments for coronary heart disease and co-morbid depression and one of primary care professionals' views on the management of patients with coronary heart disease and depression will inform the development of an intervention for this patient group. A feasibility study for a randomised controlled trial will then be conducted. DISCUSSION: This study will provide information on the relationship between coronary heart disease and depression that will allow health services to determine the efficiency of case-finding for depression in this patient group. The results of the cohort study will also provide information on risk factors for depression. The study will provide evidence on the efficacy and feasibility of a joint patient and professional led intervention and data necessary to plan a definitive randomised controlled trial of the intervention.
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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".