Bibliographic record
Abstract
OBJECTIVES: The aim was to evaluate the principal features of ossifying fibroma (OF) by systematic review (SR), and to compare their frequencies among four global groups. METHODS: The databases searched were the PubMed interface of MEDLINE and LILACS. Only those reports of OFs that occurred in a series in the reporting authors' caseload were considered. All cases were confirmed fibro-osseous lesions histopathologically. The SR-included series had also to have used radiographs. RESULTS: Of the 64 reports (including the Hong Kong report) considered, 32 reports and a total of 781 cases were included in the SR. Ten SR-included series were in languages other than English. OF affected females more frequently, but was three times more prevalent in the mandible. The mean age at first presentation was 31 years. The decade with the greatest frequency was the fourth. Females were in the majority except in the first decade. The main symptom in 66% of all SR-included cases was swelling (including deformation of the jaws). 31% were found incidentally. 84% of cases displayed buccolingual expansion; half of the mandibular cases exhibited downward displacement of the lower border of the mandible and 90% of maxillary cases involved the maxillary antrum. Only 28% of reports included follow-up; 12% of cases recurred or were reactivated. CONCLUSIONS: Although long-term follow-up of large series that would have revealed the long-term outcomes of OF was lacking, a 12% recurrence rate is clinically significant and suggests that OFs should be considered for long-term follow-up.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".