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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.003 | 0.002 |
| 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".