Corticosteroids for the Treatment of Internal Temporomandibular Joint Disorders: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Background: We evaluated the comparative effectiveness of all intra-articular injection corticosteroids for treating internal temporomandibular joint (TMJ) disorders. Methods: We searched MEDLINE, CENTRAL, EMBASE, SCOPUS, and LILACS through December 2023. We included randomized clinical trials (RCTs) enrolling patients with symptomatic internal disorders of the TMJ comparing any type of intra-articular corticosteroid therapy against another or to another minimally invasive therapy. The outcomes of interest were pain, range of mandibular motion (RoM), quality of life (QoL) and adverse effects at 1, 3, 6, and 12 months. We assessed the risk of bias using the Cochrane Collaboration’s tool. We conducted a frequentist network meta-analysis and assessed the certainty of the evidence (CoE) using GRADE. Results: We included 20 RCTs enrolling 810 participants, which assessed five corticosteroids alone or combined with arthrocentesis or hyaluronic acid. Based on moderate CoE, betamethasone is among the most effective corticosteroids for reducing pain at one (mean difference compared to arthrocentesis [MD], −3.80; 95% confidence interval [CI], −4.55 to −3.05) and three months (MD, −2.74; 95%CI, −3.42 to −2.06), and arthrocentesis plus dexamethasone at six months (MD, −0.80; 95%CI, −1.57 to −0.03). There was no convincing evidence that any intervention was better than arthrocentesis for improving the RoM and QoL at any follow-up time. Methylprednisolone may be more harmful than arthrocentesis for adverse effects. Discussion: Betamethasone and arthrocentesis plus dexamethasone are the most effective in managing pain in the short and medium term compared to arthrocentesis (moderate CoE). Decisions about their use should consider other factors, such as costs, feasibility, and acceptability. Future research should consider QoL as an outcome and assess participants at longer follow-up periods.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".