Pattern of Oral and Maxillofacial Biopsy Results in a Teaching Hospital; An 11-year Retrospective Study
Bibliographic record
Abstract
Background Biopsy remains a cornerstone in diagnostic pathology, enabling definitive diagnosis, guiding treatment planning, and informing tumor classification. This study evaluated the distribution of histopathologically diagnosed lesions in the oral and maxillofacial region over an 11-year period. Methods A retrospective review was conducted at the Department of Oral and Maxillofacial Surgery, Oral Pathology, and Molecular Biology, Lagos University Teaching Hospital, Nigeria. Biopsy records from January 2013 to December 2023 were analyzed. Ethical approval was obtained from the Health Research and Ethics Committee (HREC Approval No. ADM/DSCST/HREC/APP/5714). Results A total of 756 biopsies were reviewed. Females accounted for 52.2% of cases. The highest frequency of tumors occurred in the fourth decade of life (20.8%). Odontogenic tumors were the most prevalent lesion group, with ameloblastoma being the most common histologic diagnosis, predominantly involving the mandible. Conclusion A broad spectrum of lesions affects the maxillofacial region. Biopsy remains indispensable for definitive diagnosis. Knowledge of lesion distribution patterns is vital for clinical decision-making among oral surgeons and pathologists.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".