<b>FIVE-YEAR MULTICENTER PROSPECTIVE COHORT STUDY INTEGRATING SALIVARY BIOMARKERS AND ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND PROGNOSTIC STRATIFICATION OF ORAL SQUAMOUS CELL CARCINOMA IN PAKISTAN</b>
Bibliographic record
Abstract
Background: Oral squamous cell carcinoma (OSCC) poses a significant health burden due to delayed diagnosis. Salivary IL-8, miRNA-21, and EGFR are promising non-invasive biomarkers for tumor detection. Integration with artificial intelligence (AI) may optimize early diagnosis and prognostic assessment. Methods: This five-year multicenter prospective cohort study (2020–2025) included 480 participants (240 biopsy-confirmed OSCC cases and 240 controls) recruited from tertiary public and accredited private centers across Pakistan. Salivary IL-8 and EGFR were quantified using ELISA, while miRNA-21 was measured via qRT-PCR. Logistic regression and random forest models were developed with 5-fold cross-validation. OSCC patients were followed for five years to assess recurrence and overall survival. Diagnostic accuracy was evaluated using ROC curve analysis, and prognostic associations were assessed using Cox proportional hazards regression and Kaplan–Meier survival analysis. Results: All biomarkers were significantly elevated in OSCC patients compared to controls (p<0.001). The AI-integrated model demonstrated an AUC of 0.96, sensitivity of 93%, and specificity of 91%. Elevated composite biomarker risk scores were independently associated with reduced overall survival (HR=2.4; 95% CI: 1.6–3.8; p<0.001). Kaplan–Meier analysis showed significantly lower five-year survival in high-risk patients (log-rank p<0.001).Conclusion: AI-integrated salivary biomarkers demonstrate strong diagnostic accuracy and independent prognostic value in OSCC, supporting development of a scalable non-invasive screening model for low-resource settings.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".