Salivary biomarkers and temporomandibular disorders: A systematic review
Bibliographic record
Abstract
Temporomandibular disorders (TMD) are a common condition affecting the musculoskeletal group evoking clinical signs such as pain, restricted mouth opening, and disability in the temporomandibular joint (TMJ), masticatory musculature, and the osseous structures in the surroundings. Saliva is a strong proponent of a diagnostic and prognostic tool for TMDs. Hence, a systematic review was undertaken to answer the research question "What is the role of salivary biomarkers in the identification of TMD?" A thorough literature search was performed in databases of PubMed, Embase, and Google Scholar till February 2022. Every included study was characterized by Study ID, location, sample size, demographic information, biomarker analysis, assessment method, and results. Newcastle-Ottawa scale was used to assess the methodological quality of all qualifying research. A total of eight articles were included for the review after screening the titles, abstracts, and full-text articles. The review included articles of observational design with a control group. TMD disorders were confirmed both clinically and radiographically in the study of Shoukri et al. TMDs are commonly prevalent in maxillofacial conditions. Despite the availability of various diagnostic techniques, certain limitations are remarkable. The researchers are yet to ascertain a gold standard biomarker to identify TMD.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".