Association between orthodontic treatment and the occurrence of temporomandibular disorders: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Temporomandibular disorders (TMD) are related to a series of clinical manifestations that appear in the form of pain. Currently, there is controversy about the appearance of TMDs after orthodontic treatment. Therefore, the objective of the present study was to determine the association between orthodontic treatment and the occurrence of Temporomandibular Disorders (TMD). Material and Methods: A bibliographic search was carried out until April 2022, in the following electronic databases: Pubmed/Medline, Scopus, Scielo, Google Scholar and Web of Science. We included studies that were case-control and cohort studies, dealing with the association between orthodontic treatment and TMD, in English and Spanish, and with no time limit. The Newcastle-Ottawa scale was used to assess risk in the included studies. In addition, RevMan 5.3 was considered for meta-analysis, using as a measure the ODDS ratio in a random-effects model with a 95% confidence interval. Results: The preliminary search yielded a total of 686 articles, discarding those that did not meet the selection criteria, leaving only 6 articles. These studies reported that there is a significant association between orthodontic treatment and the occurrence of TMD, equivalent to an ODDS ratio of 1.84 with a confidence interval of 1.19-2.83. Conclusions: Orthodontic treatment, temporomandibular disorders, orthodontics, cases and controls, cohorts, review, Meta-Analysis.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".