Treating Concurrent Chronic Low Back Pain and Depression with Low-Dose Venlafaxine: An Initial Identification of “Easy-to-Use” Clinical Predictors of Early Response
Bibliographic record
Abstract
OBJECTIVE: Depression and chronic low back pain (CLBP) are both frequent and commonly comorbid in older adults seeking primary care. Serotonin-norepinephrine reuptake inhibitors (SNRIs) such as venlafaxine may be effective in treating comorbid depression and CLBP. For patients with comorbid depression and CLBP, our goal was to identify "easy-to-use" early clinical variables associated with response to 6 weeks of low-dose venlafaxine pharmacotherapy that could be used to construct a clinically useful predictive model in future studies. METHODS: We report data from the first 140 patients completing phase 1 of the Addressing Depression and Pain Together clinical trial. Patients aged ≥60 with concurrent depression and CLBP received 6 weeks of open-label venlafaxine 150 mg/day and supportive management. Using univariate and multivariate methods, we examined a variety of clinical predictors and their association with response to both depression and CLBP; change in depression; and change in pain scores at 6 weeks. RESULTS: About 26.4% of patients responded for both depression and pain with venlafaxine. Early improvement in pain at 2 weeks predicted improved response rates (P = 0.027). Similarly, positive changes in depression and pain at 2 weeks independently predicted continued improvement at 6 weeks in depression and pain, respectively (P < 0.001). CONCLUSIONS: An important minority of patients benefitted from 6 weeks of venlafaxine 150 mg/day. Early improvement in depression and pain at 2 weeks may predict continued improvement at week 6. Future studies must examine whether patients who have a poor initial response may benefit from increasing the SNRI dose, switching, or augmenting with other treatments after 2 weeks of pharmacotherapy.
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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 | 0.014 | 0.016 |
| 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 | 0.001 |
| 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".