Loss of heterozygosity: a potential tool in management of oral premalignant lesions?
Bibliographic record
Abstract
Oral leukoplakia, a heterogeneous group of lesions, demonstrates a varying degree of risk for cancer progression. Histology (presence and degree of dysplasia), the current gold standard for assessing this risk, is reasonably effective in judging the malignant risk of high-grade pre-invasive lesions. It is, however, a poor predictor for lesions without dysplasia, or with minimal dysplasia, as only a few of these lesions will progress to cancer. This poses an enormous dilemma for clinicians as to whether these lesions should be aggressively treated or not. Recent studies show that loss of specific chromosomal regions (loss of heterozygosity, LOH) that contain known or presumptive tumor suppressor genes is an early predictor of subsequent progression of oral premalignant lesions. Incorporation of LOH findings into staging of oral premalignancy could improve our ability to identify and manage high-risk premalignant lesions, particularly those with relatively benign histology but high-risk genetic changes (high-risk LOH pattern).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".