Is There an Association between Temporomandibular Disorders and Articular Eminence Inclination? A Systematic Review
Bibliographic record
Abstract
(1) Background: In order to determine the correlation between the inclination of articular eminence (AEI) and the development of temporomandibular disorders (TMDs), a systematic review was performed. (2) Methods: A systematic literature research was conducted between 1946 and January 2020, based on the following electronic databases: PubMed, Cochrane Library, Embase, Medline, Scope, SciELO, and Lilacs. Observational studies, analytical case-control studies, and cohort studies written in English were identified. The articles were selected and analyzed by two authors independently. The PICO format was used to analyze the studies and the Newcastle-Ottawa Scale (NOS) was used to verify the quality of the evidence. (3) Results: Sixteen articles were included in this review, ten case-control studies and six cohort studies. Eight articles (50%) established a positive relation between AEI and TMDs and eight (50%) did not. The scientific quality was medium-low, mainly influenced by the exposure to the risk of bias and the lack of clinical methods with adequate consistency and sensitivity on the diagnosis of TMDs. (4) Conclusions: It is controversial to establish a causal relationship between the TMDs and the AEI in the field of stomatology, due to limited and inconclusive evidence. However, it is suggested that the AEI defined by some specific methods may be associated with some special pathological stages of TMDs. High-quality prospective studies are required to draw any definitive conclusions.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".