Is there any evidence to support the use of anti-depressants in painful rheumatological conditions? Systematic review of pharmacological and clinical studies
Bibliographic record
Abstract
The aim of this study was to review the evidence supporting the use of anti-depressants in painful rheumatological conditions. A systematic review of papers published between 1966 and 2007, in five European languages, on anti-depressants in rheumatological conditions was performed. Papers were scored using Jadad method and analgesic ES was calculated. We selected 78 clinical studies and 12 meta-analyses, from 140 papers. The strongest evidence of an analgesic effect of anti-depressants has been obtained for fibromyalgia. A weak analgesic effect is observed for chronic low back pain, with an efficacy level close to that of analgesics. In RA and AS, there is no analgesic effect of anti-depressants, but these drugs may help to manage fatigue and sleep disorders. There is no clear evidence of an analgesic effect inOA, but studies have poor methodological quality. Analgesic effects of anti-depressants are independent of their anti-depressant effects. Tricyclic anti-depressants (TCAs), even at low doses, have analgesic effects equivalent to those of serotonin and noradrenalin reuptake inhibitors (SNRIs), but are less well tolerated. Selective serotonin reuptake inhibitors (SSRIs) have modest analgesic effects, but higher doses are required to achieve analgesia. Anti-depressant drugs, particularly TCAs and SNRIs, have analgesic effects in chronic rheumatic painful states in which analgesics and NSAIDs are not very efficient, such as fibromyalgia and chronic low back pain. In inflammatory rheumatic diseases, anti-depressants may be useful for managing fatigue and sleep disorders. Further studies are required to compare anti-depressants with other analgesics in the management of chronic painful rheumatological conditions.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".