ORAL SQUAMOUS CELL CARCINOMA
Bibliographic record
Abstract
Background: The aim of this study was to evaluate clinico-pathologicalparameters and the status of argyrophilic nucleolar organizer regions stain in various histologicalgrades of oral squamous cell carcinoma. Materials and Methods: A cross sectional studywas conducted on fifty cases of oral squamous cell carcinoma. The specimen were collectedfrom the department of Oral & Maxillofacial Surgery and processed for hematoxylin and eosinstain and AgNOR stain Pathology Laboratory, King Edward Medical University Lahore. Results:Bidi smoking is associated with oral squamous cell carcinoma. The AgNOR (mAgNOR andpAgNOR) status was significantly low in well differentiated and moderately differentiatedcompared to poorly differentiated oral squamous cell carcinoma (p =0.001). AgNOR size inpoorly differentiated was significantly higher than the AgNOR size in well differentiated oralsquamous cell carcinoma. Similarly the distribution of AgNOR in moderately and poorlydifferentiated oral squamous cell carcinoma was significantly high. The AgNORs index wassignificantly high in poorly differentiated squamous cell carcinoma as compared to welldifferentiated and moderately differentiated squamous cell carcinoma. Conclusions: The useof AgNORs stain is easy, valid and reliable method to assess the histological grading of oralsquamous cell carcinoma and should be used to predict the prognosis of patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".