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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".