Somatic symptom overlap in Beck Depression Inventory–II scores following myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Depression measures that include somatic symptoms may inflate severity estimates among medically ill patients, including those with cardiovascular disease. AIMS: To evaluate whether people receiving in-patient treatment following acute myocardial infarction (AMI) had higher somatic symptom scores on the Beck Depression Inventory-II (BDI-II) than a non-medically ill control group matched on cognitive/affective scores. METHOD: Somatic scores on the BDI-II were compared between 209 patients admitted to hospital following an AMI and 209 psychiatry out-patients matched on gender, age and cognitive/affective scores, and between 366 post-AMI patients and 366 undergraduate students matched on gender and cognitive/affective scores. RESULTS: Somatic symptoms accounted for 44.1% of total BDI-II score for the 209 post-AMI and psychiatry out-patient groups, 52.7% for the 366 post-AMI patients and 46.4% for the students. Post-AMI patients had somatic scores on average 1.1 points higher than the students (P<0.001). Across groups, somatic scores accounted for approximately 70% of low total scores (BDI-II <4) v. approximately 35% in patients with total BDI-II scores of 12 or more. CONCLUSIONS: Our findings contradict assertions that self-report depressive symptom measures inflate severity scores in post-AMI patients. However, the preponderance of somatic symptoms at low score levels across groups suggests that BDI-II scores may include a small amount of somatic symptom variance not necessarily related to depression in post-AMI and non-medically ill respondents.
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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".