Temporomandibular Joint Disorders and Pain Confounders: An Awareness Study
Bibliographic record
Abstract
OBJECTIVES: Temporomandibular disorders (TMD) are the most common nonodontogenic cause of orofacial pain, leading to morbidity and impairment. TMD presents a diagnostic challenge due to many aetiologies that exhibit comparable symptoms and refer pain to the temporomandibular joint (TMJ) region. Patients may be referred to dental specialists without accounting for all pain sources. This study aims to identify radiographic confounders (RCs) that can be mistaken for TMD in patients undergoing TMJ assessment using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: A review of 369 CBCT oral maxillofacial radiology reports of the TMJ acquired between July 2020 and June 2023 was completed. Pertinent RCs were classified as endodontic lesions, impacted dentition, sinus pathologies, root fractures, soft tissue calcifications, and others. The chi-squared test assessed the significance of the relationship between RCs and patient variables. RESULTS: A total of 283 RCs were identified in 202 of the 369 cases (54.7%). The most frequent findings included sinus abnormalities (32.5%), endodontic lesions (15.2%), impacted dentition (12.7%), and elongated/calcified stylohyoid process (9.2%). Significant associations were found between sinus pathologies with TMD signs (P = .009) and gender (P = .001). CONCLUSION: Our results indicate that RCs that mimic TMD-related symptoms are prevalent in patients referred for TMJ CBCT imaging. CLINICAL RELEVANCE: Clinicians should be aware of these RCs when diagnosing complaints related to the TMJ. We recommend clinicians first obtain dental clearance and investigate all other potential sources of a patient's complaint before initiating referrals to avoid unnecessary costs and delays in patient care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".