Loss of heterozygosity: a potential tool in management of oral premalignant lesions?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it