Demographics and risk factors that influence the prevalence of depression in patients attending cardiac rehabilitation
Bibliographic record
Abstract
Depression has been associated with adverse outcomes in patients with cardiac disease. Data on its prevalence and the factors influencing it are limited in the cardiac rehabilitation program (CRP) setting. To elucidate the prevalence of and the factors that influence depression in patients attending CRP. Patients attending the CRP from 2003 to 2016 were included in the study. All patients had a Beck Depression Inventory-II (BDI-II) performed prior to commencement in CRP and were followed longitudinally. The BDI-II for the 4989 patients were as follows: 0 to 13 (normal) = 3623 (72%); 14 to 19 (mild depression) = 982 (20%); 20 to 28 (moderate depression) = 299 (6%); 29 to 63 (severe depression) = 85 (2%). The BDI-II (mean ± SEM) for males (mean age: 60.8 ± 0.1 years) and females (mean age: 63.4 ± 0.3 years, P < .001) were 7.0 ± 0.1 and 8.5 ± 0.2 (P < .001), respectively. Elevated BDI-II scores (14-63) were more common in type 1 (41.1%) and type 2 (30.5%) diabetics than nondiabetics (25.7%). Similarly, elevated scores were more common in smokers (36.1%) than never-smokers (24.7%). The BDI-II scores for Caucasians, South Asians, and East Asians were 7.3 ± 0.1, 8.0 ± 0.3, and 7.0 ± 0.3 respectively (P = .01 for CA vs SA by 1-way ANOVA and least significant difference test). The prevalence of depression is high in patients attending CRP affecting 28% of the population. BDI-II is a simple validated screening tool that can be applied to patients attending CRP. Diabetics, current smokers, and South Asians all had a higher prevalence of depression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".