The Influence of Somatic Symptoms on Beck Depression Inventory Scores in Hospitalized Postmyocardial Infarction Patients
Bibliographic record
Abstract
OBJECTIVE: The Beck Depression Inventory (BDI) has been used more than any other self-report questionnaire in research on depression in cardiovascular disease. However, no studies have examined whether BDI scores may be influenced by somatic symptoms common after myocardial infarction (MI) that may overlap with symptoms of depression. The objective of this study was to examine whether BDI scores of post-MI patients may be influenced by somatic symptoms that commonly occur after MI, but may not be related to depression. METHOD: Post-MI patients and psychiatric outpatients were matched on BDI cognitive-affective symptom scores, sex, and age, and their BDI somatic symptom scores were compared using independent samples t tests. RESULTS: Somatic symptoms accounted for 57% of BDI total scores for 296 post-MI patients (mean total BDI = 8.8), compared with 50% for 296 matched psychiatric outpatients (mean total BDI = 7.6). Overall, BDI somatic scores of post-MI patients were 1.3 points higher than for psychiatric outpatients (95% CI 0.68 to 1.82; P < 0.001), equivalent to 14% of total scores of post-MI patients. CONCLUSIONS: The relative influence of somatic symptoms on BDI total scores was higher for post-MI patients than for psychiatric outpatients matched on cognitive-affective scores, sex, and age. This finding stands in contrast to that from a previous study that used similar methods and sample comparisons and found that post-MI and psychiatric outpatients did not differ in their endorsement of somatic symptoms on the BDI-II. The BDI-II may be preferable to the BDI in post-MI patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".