Platelet Concentrate Treatments for Temporomandibular Disorders: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This systematic review compared platelet concentrates (PCs) versus hyaluronic acid (HA) or saline/Ringer's solution injections as treatments of temporomandibular osteoarthritis and disc displacement in terms of pain and maximum mouth opening (MMO). METHODS: PubMed, Cochrane, and Scopus were searched up to March 6, 2020. Inclusion criteria were randomized clinical trials (RCTs). Exclusion criteria were case series, observational studies, animal studies, and reviews. The Effective Public Health Practice Project (EPHPP) quality assessment tool was used to assess the risk of bias in the included studies. The weighted mean difference was used to compare the results. RESULTS: Nine RCTs were included with a total of 407 patients. The numbers of joints treated were 262, 112, and 112 in the PC, HA, and saline groups, respectively. The quality of studies was rated as strong in 4 studies, moderate in 4 studies, and weak in 1 study. The meta-analysis revealed that PCs decreased pain visual analogue scale (VAS) scores compared to HA by an average of -1.11 (CI, -1.62 to -0.60; P < 0.0001) and -0.57 (CI, -1.55 to 0.41; P = 0.26) at 3 and 12 mo follow-up respectively. Also, the average decrease in pain scores with PC compared to saline was -1.33 (CI, -2.61 to -0.06; P = 0.04), -2.07 (CI, -3.46 to -0.69; P = 0.003), and -2.71 (CI, -4.69 to -0.72; P = 0.008) at 3, 6, and 12 mo, respectively. Regarding MMO measurements, PC was comparable to HA, but it was significantly better than saline after 3 and 6 mo [2.9 mm (CI,1.47 to 4.3; P < 0.0001), and 1.69 mm (CI, 0.13 to 3.25; P = 0.03) respectively]. CONCLUSION: PC reduces pain VAS scores compared to HA during the first 3 m after treatment, and when compared to saline, it reduces pain and increases MMO for longer durations. However, due to differences between groups regarding PC preparation protocols and study heterogeneity, further standardized RCTs are required. KNOWLEDGE TRANSFER STATEMENT: This study provides researchers and clinicians with quantitative and qualitative analyses of the current evidence regarding the clinical outcomes of platelet concentrate injections in the treatment of temporomandibular joint osteoarthritis and disc displacement in terms of pain control and maximum mouth opening.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".