A systematic review of the clinical and radiographic features of hybrid central giant cell granuloma lesions of the jaws
Bibliographic record
Abstract
OBJECTIVE: Central giant cell granuloma (CGCG) can coexist with other benign lesions of the jaw. These hybrid lesions are diagnostically challenging to both oral pathologists and radiologists. This work systematically reviews the clinical and radiographic features of hybrid-CGCG lesions in the jaws. MATERIALS AND METHODS: Three reviewers conducted an electronic search of five databases for histologically diagnosed hybrid-CGCG lesions in human jaws. RESULTS: Thirty-four of 1224 articles met the inclusion criteria. Of 39 hybrid-CGCG lesions, 14 (35.9%) were central odontogenic fibroma, 11 (28.2%) were central ossifying fibroma, seven (17.9%) were fibrous dysplasia, and seven (17.9%) were other bone conditions. There were 22 females and 17 males with a mean age of 30.5 ± 19.9 years. 89.5% of hybrid-CGCG lesions were well defined, 57.9% were non-corticated, 60.5% were radiolucent, and 66.7% were in the posterior mandible. Most hybrid lesions affected the cortical plates by thinning, expansion, or perforation (93.1%), displaced, or resorbed teeth (60%). CONCLUSION: The radiographic features of hybrid-CGCG lesions vary according to the concurrent bony lesion. Hybrid-CGCG lesions altered the radiographic appearance with the following entities: fibrous dysplasia, melorheostosis, and Paget's disease. Optimal imaging modalities are crucial to detail radiographic features and direct representative biopsy of suspicious sites that may host a CGCG hybridisation.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".