Koilocytosis in Oral Squamous Cell Carcinoma: What Does It Mean?
Bibliographic record
Abstract
OBJECTIVES: (1) To determine the prognosis of patients with squamous cell carcinoma (SCC) of the oral cavity and (2) to determine whether a relationship exists between koilocytosis and tumour grade, stage, and prognosis in these patients. METHODS: Retrospective study of patients with SCC of the oral cavity at McGill University in the last 5 years followed by a pathology review of available cases. The data extracted were age at diagnosis, risk factors, tumour stage and grade, koilocytosis, treatment, and outcome. Deoxyribonucleic acid (DNA) extraction was done, followed by polymerase chain reaction (PCR) to detect the presence of human papillomavirus (HPV). RESULTS: One hundred ninety-nine patients were identified. There was an overall 13% mortality rate over the follow-up period of 5 years. Pathology material was available for 146 patients. Koilocytosis was found in 67% of patients. More specifically, koilocytosis was present in 67.2% of well-differentiated lesions, 65.2% of moderately differentiated lesions, and 68.4% of poorly differentiated lesions. Koilocytosis was evenly distributed among tumour stages (I, 73%; II, 68%; III, 68%; IV, 69%). PCR was positive in only one sample. CONCLUSION: Mortality rates in this series are similar to those published in the literature. There is a high prevalence of koilocytosis in SCC of the oral cavity, which appears to be associated with better tumour differentiation and poorly differentiated equally and also evenly distributed among tumour. Because HPV is negative, we do not know if koilocytosis is a marker of HPV. We could not determine the relationship between HPV and prognosis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".