The correlation between tumour volume and survival in oral cavity and oropharyngeal squamous cell carcinoma /
Bibliographic record
Abstract
The Tumour-Node-Metastasis (TNM) classification system of tumour stage does not always reflect the actual tumour mass present at diagnosis. Recent reports propose that volumetric analysis may allow improved stratification of disease recurrence and survival in head and neck squamous cell cancer (SCC). This study aims to assess the prognostic value of tumour volume on the outcome of patients with oral cavity and oropharyngeal SCC. A retrospective review of 73 patients was completed. Tumours were outlined semi-automatically in digitized computed tomography scans, and volumes computed based on surface triangulations of three-dimensional reconstructions with novel software developed at McGill. Results illustrate significant interstage variability within the current TNM model. Moreover, in oral cavity and oropharyngeal SCC, tumour volume as well as T-stage are significant and independent predictors of disease free survival and overall survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".