Keratocystic odontogenic tumour in a Hong Kong community: the clinical and radiological features
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the clinical and conventional radiological features of a consecutive series of cases of "keratocystic odontogenic tumour" (KCOT) affecting a Hong Kong Chinese community and to determine their outcome by follow-up. METHODS: All cases were accompanied by appropriate radiography and were histopathologically confirmed. RESULTS: 33 consecutive KCOTs were reviewed. 18 patients were male. The mean age at first presentation was 30.6 years. Swelling was the most frequent presenting symptom. Those patients first presenting with pain were significantly older, whereas those first presenting with a maxillary lesion were significantly younger. The maxilla and mandible were affected in 13 and 20 cases, respectively. KCOTs were most frequently confined to the posterior sextants of both jaws. KCOTs affecting the maxilla were mainly unilocular, whereas those affecting the mandible were multilocular. Patients with multilocular KCOTs were significantly older. Patients with KCOTs associated with root resorption were significantly older, whereas patients associated with unerupted teeth were significantly younger. 69% displaced teeth, 41% resorbed them and 56% were associated with unerupted teeth. All but two were followed up for at least 2 years. Three lesions recurred. CONCLUSIONS: KCOTs in this community displayed some differences from those reported in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".