Duloxetine and care management treatment of older adults with comorbid major depressive disorder and chronic low back pain: results of an open‐label pilot study
Bibliographic record
Abstract
OBJECTIVE: In older adults, major depressive disorder (MDD) and chronic low back pain (CLBP) are common and mutually exacerbating. We predicted that duloxetine pharmacotherapy and Depression and Pain Care Management (DPCM) would result in (1) significant improvement in MDD and CLBP and (2) significant improvements in health-related quality of life, anxiety, disability, self-efficacy, and sleep quality. DESIGN AND INTERVENTION: Twelve week open-label study using duloxetine up to 120 mg/day + DPCM. SETTING: Outpatient late-life depression research clinic. PATIENTS: Thirty community-dwelling adults >60 years old. OUTCOME MEASURES: Montgomery Asberg Depression Rating Scale (MADRS) and McGill Pain Questionnaire-Short Form (MPQ-SF). RESULTS: 46.7% (n = 14) of the sample had a depression remission. All subjects who met criteria for the depression remission also had a pain response. 93.3% (n = 28) had a significant pain response. Of the subjects who met criteria for a low back pain response, 50% (n = 14) also met criteria for the depression remission. The mean time to depression remission was 7.6 (SE = 0.6) weeks. The mean time to pain response was 2.8 (SE = 0.5) weeks. There were significant improvements in mental health-related quality of life, anxiety, sleep quality, somatic complaints, and both self-efficacy for pain management and for coping with symptoms. Physical health-related quality of life, back pain-related disability, and self-efficacy for physical functioning did not improve. CONCLUSIONS: Serotonin and norepinephrine reuptake inhibitors like duloxetine delivered with DPCM may be a good choice to treat these linked conditions in older adults. Treatments that target low self-efficacy for physical function and improving disability may further increase response rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".