P2559Prevalence of and the influence of gender and ethnicity on depression in patients attending a cardiac rehabilitation program
Bibliographic record
Abstract
Background: Depression has been shown to adversely affect outcomes in patients with cardiac disease. It remains underrecognized and undertreated. Further, data on the prevalence of depression and the factors influencing it are limited. The Beck Depression Inventory (BDI) has been validated as a tool to screen for underlying depression. Purpose: The purpose of the present study was to elucidate the prevalence of and the factors that influence depression in patients attending cardiac rehabilitation. Methods: Patients attending the cardiac rehabilitation program (CRP) from 2003–2016 were included in the study. All patients had a BDI performed prior to commencement in CRP. All data were collected prospectively and entered into a SPSS database. Results: The BDI for the 5065 pts (mean age: 61.3±0.1 years; Males = 76.2%) was: 0–9 (normal) = 3665 (72.4%); 10–18 (mild depression) = 1007 (19.9%); 19–29 (moderate depression) = 306 (6.0%); 30–63 (severe depression) = 87 (1.7%). The BDI (mean ± SEM) for males (mean age: 60.8±0.1yrs) and females (mean age: 63.4±0.3yrs, p<0.001) were 7.0±0.1 and 8.5±0.2 (p<0.001), respectively. The BDI scores for Caucasians (CA), South Asians (SA), and East Asians (EA) were 7.3±0.1, 8.0±0.3, and 7.0±0.3 respectively (p=0.01 for CA versus SA by one-way ANOVA and least significant difference test). A BDI of 10 or more was observed in 26.8%, 32.0%, and 23.1% of CA, SA, and EA respectively (p=0.01). The mean ages for CA, SA, and EA were 61.6±0.1yrs, 60.5±0.3yrs and 59.8±0.8yrs respectively. The proportion of females in the CA, SA, and EA were 24.0%, 22.5%, and 22.4% respectively (p>0.05).
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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 | 0.000 | 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) | 0.005 | 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".