Bibliographic record
Abstract
OBJECTIVES: The aim of this review is to evaluate the principal clinical and conventional radiographic features of non-syndromic keratocystic odontogenic tumour (KCOT) by systematic review (SR), and to compare the frequencies between four global groups. METHODS: The databases searched were the PubMed interface of Medline and LILACS. Only those reports of KCOTs that occurred in a series of consecutive cases, in the reporting authors' caseload, were considered. RESULTS: 51 reports, of 49 series of cases, were included in the SR. 11 SR-included series were in languages other than English. KCOTs affected males more frequently and were three times more prevalent in the mandible. Although the mean age at first presentation was 37 years, the largest proportion of cases first presented in the third decade. The main symptom was swelling. Over a third were found incidentally. Nearly two-thirds displayed buccolingual expansion. Over a quarter of cases recurred. Only a quarter of all SR-included reported series of cases included details of at least one radiological feature. The East Asian global group presented significantly as well-defined, even corticated, multilocular radiolucencies with buccolingual expansion. The KCOTs affecting the Western global group significantly displayed an association with unerupted teeth. CONCLUSIONS: Long-term follow-up of large series that would have revealed detailed radiographic description and long-term outcomes of non-syndromic KCOT was lacking.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".