Collaborative Care Versus Screening and Follow-up for Patients With Diabetes and Depressive Symptoms: Results of a Primary Care–Based Comparative Effectiveness Trial
Bibliographic record
Abstract
OBJECTIVE: Depressive symptoms are common and, when coexisting with diabetes, worsen outcomes and increase health care costs. We evaluated a nurse case-manager-based collaborative primary care team model to improve depressive symptoms in diabetic patients. RESEARCH DESIGN AND METHODS: We conducted a controlled implementation trial in four nonmetropolitan primary care networks. Eligible patients had type 2 diabetes and screened positive for depressive symptoms, based on a Patient Health Questionnaire (PHQ) score of ≥10. Patients were allocated using an "on-off" monthly time series. Intervention consisted of case-managers working 1:1 with patients to deliver individualized care. The main outcome was improvement in PHQ scores at 12 months. A concurrent cohort of 71 comparable patients was used as nonscreened usual care control subjects. RESULTS: Of 1,924 patients screened, 476 (25%) had a PHQ score >10. Of these, 95 were allocated to intervention and 62 to active control. There were no baseline differences between groups: mean age was 57.8 years, 55% were women, and the mean PHQ score was 14.5 (SD 3.7). Intervention patients had greater 12-month improvements in PHQ (7.3 [SD 5.6]) compared with active-control subjects (5.2 [SD 5.7], P = 0.015). Recovery of depressive symptoms (i.e., PHQ reduced by 50%) was greater among intervention patients (61% vs. 44%, P = 0.03). Compared with trial patients, nonscreened control subjects had significantly less improvement at 12 months in the PHQ score (3.2 [SD 4.9]) and lower rates of recovery (24%, P < 0.05 for both). CONCLUSIONS: In patients with type 2 diabetes who screened positive for depressive symptoms, collaborative care improved depressive symptoms, but physician notification and follow-up was also a clinically effective initial strategy compared with usual care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".