Clinicopathological risk factors of oral second primary tumours
Bibliographic record
Abstract
Background: Oral second primary tumours (SPTs) have a poor prognosis due to late-stage diagnosis. This study evaluates the demographic and clinicopathological risk predictors of SPTs. Methods: Patients with oral squamous cell carcinoma, carcinoma in situ, or severe dysplasia were accrued into the Oral Cancer Prediction Longitudinal study within one year post-curative treatment. Data on demographics, risk habits, and primary tumour characteristics were collected. Clinical follow-up included assessing the presence of second oral premalignant lesions (SOPLs), clinicopathological features, and the results from toluidine blue staining and fluorescence visualization. Results: Among 296 patients, 23 (8 %) developed SPTs. Older age at primary cancer diagnosis (P = 0.008) and a history of chewing tobacco or betel nut (P = 0.043) increased the risk of SPTs. Patients with primary tumours located at low-risk sites had an increased risk of SPTs (P = 0.004), which often presented at high-risk sites. The presence of SOPLs (P < 0.001), and multiple lesions (P = 0.017) significantly increased the risk of SPTs. Positive toluidine blue staining indicated a trend toward higher risk of SPTs, whereas fluorescence visualization did not. The median time to SPT diagnosis was 3.25 years post-treatment. Conclusions: Identifying second or multiple oral premalignant lesions is critical for predicting the risk of SPTs regardless of their clinical or histological characteristics. Routine biopsy of these lesions should be prioritized to ensure timely diagnosis. Incorporating these risk predictors into clinical follow-up can enhance early cancer detection and improve patient outcomes.
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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 | 0.000 | 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) | 0.003 | 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".