Exploring the Association Between Clinical Features and CBCT Findings in TMJ Degenerative Joint Disease
Bibliographic record
Abstract
BACKGROUND: Temporomandibular joint (TMJ) degenerative joint disease (DJD) involves progressive osseous changes and is commonly associated with temporomandibular disorders (TMD). Cone-beam computed tomography (CBCT) is a valuable diagnostic tool for evaluating these changes. However, the relationship between clinical signs and symptoms, such as TMJ clicking or pain and radiographic findings remains poorly understood. Clarifying these associations can refine imaging prescribing practices and improve patient-specific diagnostic strategies. OBJECTIVE: This study aimed to investigate the association between clinical signs and symptoms of TMD and radiographic features of TMJ DJD detected on CBCT, emphasising its diagnostic value and limitations. METHODS: A retrospective chart review of 98 patients (196 TMJs) was conducted at a university-based oral medicine clinic. Clinical signs, including TMJ clicking, muscle pain and joint pain, were documented and CBCT findings, such as osteophytes and erosions, were analysed. Logistic regression was used to assess associations. RESULTS: A significant association was identified between TMJ clicking and the presence of osteophytes (p < 0.05). No significant associations were observed between other clinical features, including muscle and joint pain and CBCT findings. CONCLUSION: The findings support an indication-driven approach to CBCT imaging, highlighting its diagnostic value in patients with specific clinical presentations, such as TMJ clicking, combined with additional clinical indicators. Routine CBCT imaging for all patients with TMD is not justified and future research should focus on refining imaging guidelines to ensure judicious use in TMJ diagnostics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".