Role of different imaging modalities in assessment of temporomandibular joint erosions and osteophytes: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To evaluate the ability of different diagnostic imaging techniques for diagnosing the presence of erosions and osteophytes in the temporomandibular joint (TMJ). METHODS: A systematic search of PubMed, Medline, all Evidence Based Medicine (EBM) reviews, Embase, Web of Sciences and Lilacs identified nine articles that met the selection criteria: some type of TMJ diagnostic imaging, data from autopsy or dry skull TMJs as gold standard, absence of diagnosed systemic arthritis and evaluation of the presence of erosions and/or osteophytes. A hand search of the references of the selected articles was also performed. RESULTS: Selected studies evaluated panoramic imaging (unenhanced and colour-enhanced digital subtraction panoramic imaging), axially corrected sagittal tomography, axially corrected frontal tomography, sagittal MRI, CT, high-resolution ultrasound and cone beam CT (CBCT). CONCLUSIONS: Axially corrected sagittal tomography is currently the imaging modality of choice for diagnosing erosions and osteophytes in the TMJ. CT does not seem to add any significant information to what is obtained from axially corrected sagittal tomography. CBCT might prove to be a cost- and radiation dose-effective alternative to axially corrected sagittal tomography. Combining different radiographic techniques is likely to be more accurate in diagnosing erosions and osteophytes in the TMJ than using a single imaging modality. Diagnostic studies that simultaneously evaluate all of the available TMJ imaging technologies are needed.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".