How pathological criteria can impact prognosis of tongue and floor of the mouth squamous cell carcinoma
Bibliographic record
Abstract
other: Pathological parameters have been indicated as tumor prognostic factors in oral carcinoma. OBJECTIVE: The objective of this study was to investigate the impact of pathological parameters on prognosis of patients affected only by tongue and/or floor of the mouth squamous cell carcinoma (SCC). METHODOLOGY: In total, 380 patients treated in the Brazilian National Cancer Institute (INCA) from 1999 to 2006 were included. These patients underwent radical resection followed by neck dissection. The clinical and pathological characteristics were recorded. The Kaplan-Meier method and Cox proportional hazards model were used in survival analysis. Overall survival (OS), cancer-specific survival (CSS) and disease-free interval (DFI) were estimated. Cox residuals were evaluated using the R software version 3.5.2. Worst OS, CSS and DFI were observed in patients with tumors in advanced pathological stages (p<0.001), with the presence of perineural invasion (p<0.001) and vascular invasion (p=0.005). RESULTS: Advanced pathological stage and the presence of a poorly differentiated tumor were independent prognostic factors for OS and CSS. However, advanced pathological stage and perineural invasion were independent predictors of a shorter OS, DFI and CSS. CONCLUSION: Pathological stage and perineural invasion were the most significant pathological variables in survival analysis in tongue and/or floor of the mouth SCC.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".