Antidepressants in the Treatment for Chronic Low Back Pain: Questioning the Validity of Meta‐Analyses
Bibliographic record
Abstract
OBJECTIVES: To contrast the analgesic effect of duloxetine with antidepressants reported in other published randomized clinical trials (RCTs) and review articles in patients with chronic low back pain (CLBP). METHODS: In this narrative review, the results of 13 RCTs and 5 systematic reviews examining the analgesic effect of various antidepressants in CLBP were contrasted with those of 3 placebo-controlled duloxetine RCTs. Treatment effects based on the Brief Pain Inventory (BPI) average score in the duloxetine RCTs were assessed in all completers (by study and overall) and in last-observation-carried-forward (LOCF) analyses (extracted from study reports). 30%- and 50%-reduction response rates were compared between duloxetine and placebo. RESULTS: Eleven different antidepressants were examined in 13 individual RCTs. Sample sizes, treatment durations, and analysis methods varied across studies. Reviews each included 5 to 9 of the RCTs and came to different conclusions regarding the analgesic effect of antidepressants: 2 found no evidence while 3 reported some evidence. The completer analysis showed greater improvements in BPI average scores with duloxetine vs. placebo (significant in 2 studies). Overall, the least square mean (standard error) difference between treatments was - 0.7 (0.15) (P < 0.0001). Overall response rates were significantly larger with duloxetine than with placebo. CONCLUSIONS: Due to the diversity of previous studies and the pooling methods used, the conclusions regarding the analgesic effect of antidepressants in CLBP drawn from systematic reviews must be interpreted with caution. Appropriately designed and powered studies similar to recently published duloxetine studies are recommended to demonstrate the analgesic effect of antidepressants.
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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 | 0.245 | 0.475 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".