Beyond Depression: The Role of Antidepressants in Managing Chronic Temporomandibular Disorders. A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chronic temporomandibular disorder (TMD) pain significantly impairs quality of life and lacks universally effective treatments. Antidepressants, traditionally used for mood disorders, have shown potential in managing chronic pain conditions. This systematic review evaluates the efficacy and safety of antidepressants for chronic TMD pain management. METHODS: Eligibility criteria: Included randomised controlled trials (RCTs) assessing antidepressants for chronic TMD pain in adults, reporting pain reduction or functional improvement as outcomes. INFORMATION SOURCES: Searches were conducted in MEDLINE, Embase, CINAHL, Scopus, Web of Science, and Cochrane Library through April 2024. Risk of bias: The Cochrane Risk of Bias 2 tool was used to assess study quality. SYNTHESIS OF RESULTS: Narrative synthesis was performed due to heterogeneity in interventions and outcomes. RESULTS: Included studies: Seven RCTs with sample sizes ranging from 12 to 80 participants. Studies evaluated various antidepressants, including amitriptyline, duloxetine, nortriptyline, and citalopram, alone or combined with non-pharmacological treatments. SYNTHESIS OF RESULTS: Amitriptyline and duloxetine demonstrated significant reductions in pain intensity when used in combination therapies. Functional improvements, such as increased mouth opening, were observed in some studies. Side effects, particularly with duloxetine, were more frequent than with placebo. Variability in study designs, populations, and outcome measures limited comparability. Small sample sizes, short follow-up durations, and heterogeneity in interventions and outcomes reduced the strength of evidence. CONCLUSION: Antidepressants, particularly when combined with non-pharmacological treatments, may enhance pain relief and functional outcomes for chronic TMD pain. However, high-quality, long-term studies are needed.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".