TNM‐based stage groupings in head and neck cancer: Application in cancer of the hypopharynx
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to test the Union Internationale Contre le Cancer (UICC)/TNM category-based head and neck cancer stage grouping systems proposed in the literature for their ability to create clinically relevant prognostic groups of like-patients with cancer of the hypopharynx. METHODS: Population-based retrospective survival study of 595 patients with squamous cell carcinoma of the hypopharynx across Ontario, Canada, from January 1990 to January 2000. The grouping systems of UICC/TNM, T and N Integer Score (TANIS), Hart, Berg, Snyderman, Kiricuta, and Hall were tested and compared for prognostic ability using hazard consistency, hazard discrimination, percent variance explained, outcome prediction, and balance. RESULTS: All 8 systems predicted disease-specific survival. The system proposed by Snyderman performed the best, and UICC/TNM sixth edition did not perform as well as most. CONCLUSION: The UICC/TNM stage group classification, although successful in creating statistically distinct groups, did not perform as well as other stage grouping systems, continuing a theme that has been reported previously.
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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 | 0.006 | 0.010 |
| 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) | 0.001 | 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".