Masticatory Function in Individuals with Temporomandibular Disorders: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol in the PubMed, Cochrane Library, LILACS, EBSCO, Scielo, between 2012 and 2022. The methodological quality was assessed by using the Newcastle-Ottawa Study Quality Assessment Scale. Mean differences and 95% confidence intervals were calculated and combined in meta-analyses. A total of 1202 participants were included in this systematic review (690 with TMD; 512 without TMD), with 22 articles being included in the qualitative analysis. Only three studies enabled the comparative analysis of the results. Ten articles showed a high methodological quality and a low risk of bias, and twelve had a low methodological quality and an increased risk of bias. The meta-analysis showed that the differences between the intervention and control groups were not statistically significant for the percentage overlapping coefficient of the anterior temporal muscle, for the masseter, and for the torque coefficient. The parameters analyzed with the compound technique for chewing showed altered mandibular functions in individuals with TMD. With the EMG method, it was possible to suggest that TMD in adult individuals causes compensatory muscle behaviors, and several changes in the masticatory function were found.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.010 | 0.010 |
| 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) | 0.004 | 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".