Clinicopathological and prognostic implications of genetic alterations in oral cancers
Bibliographic record
Abstract
This study evaluated the clinicopathological and prognostic implications of genetic alterations characterizing oral squamous cell carcinoma(OSCC). Comparative genomic hybridization(CGH) was used to identify chromosomal alterations present in primary OSCCs obtained from 97 pateints. In this population, tobacco use was a significant risk factor for OSCC. By contrast, all 97 of our samples are negative for human papillomavirus (HPV) DNA integration, which is another known risk factor for OSCC in certain populations. Results of the Fisher's exact test followed by Benjamini-Hochberg correction for multiple testing, showed a correlation of 7p gain and 8p loss with node-positive OSCC (p≤0.04 for both genetic alterations) and association of 11q13 gain with high-grade OSCC (p≤0.05). Univariate Cox-proportional hazard models, also corrected for multiple testing, showed significant association of 11q13 gain and 18q loss with decreased survival (p≤0.05). These findings were supported by multivariate analysis which revealed that 11q13 gain and 18q loss together serve as a strong bivariate predictor of poor prognosis. In conclusion, our study has identified genetic alterations that correlate significantly with nodal status, grade, and poor survival status of OSCC. These potential biomarkers may aid the current TNM system for better prediction of clinical outcome.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".