Systematic detection and multidisciplinary care of depression in older medical inpatients: a randomized trial
Bibliographic record
Abstract
Background: Major depression is a frequent and serious disorder in older medical inpatients. Because the condition goes undetected and untreated in most of these patients, we conducted a randomized clinical trial to evaluate the effectiveness of a strategy of systematic detection and multidisciplinary treatment of depression in this population. Methods: Consecutive patients aged 65 years or more admitted to general medical services in a primary care hospital between October 1999 and November 2002 were screened for depression with the Diagnostic Interview Schedule (DIS) within 48 hours after admission. Patients found to have major depression were randomly allocated to receive the intervention or usual care. The intervention involved consultation and treatment by a psychiatrist and follow-up by a research nurse and the patient9s family physician. Research assistants, blind to group allocation, collected data from the patients at enrolment and at 3 and 6 months later using the Hamilton Depression Rating Scale (HAMD), the Medical Outcomes 36-item Short Form (SF-36), the DIS, the Mini-Mental State Examination (MMSE), the Older Americans Resources and Services (OARS) questionnaire to assess basic and instrumental activities of daily living (OARS-ADL and OARS-IADL) and the Rating Scale for Side Effects. Data on the severity of illness, length of hospital stay, health services and medication use, mortality and process of care were also collected. The primary outcome measures were the HAMD and SF-36. Results: Of 1500 eligible patients who were screened, 157 were found to have major depression and consented to participate (78 in the intervention group and 79 in the usual care group). At randomization, there were no clinically or statistically significant differences between the 2 groups. Sixty-four patients completed follow-up to 6 months, 57 withdrew, and 36 died. At 6 months, there were no clinically or statistically significant differences the 2 groups in HAMD or SF-36 scores or any of the secondary outcome measures. Interpretation: We were unable to demonstrate that systematic detection and multidisciplinary care of depression was more beneficial than usual care for elderly medical inpatients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".