A comparison of published head and neck stage groupings in carcinomas of the oral cavity
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The combination of T, N, and M classifications into stage groupings is meant to facilitate a number of activities, including the estimation of prognosis and the comparison of therapeutic interventions among similar groups of cases. We tested the UICC/AJCC 5th edition stage grouping and seven other TNM-based groupings proposed for head and neck cancer for their ability to meet these expectations in a specific site: carcinomas of the oral cavity. METHODS: We defined four criteria to assess each grouping scheme: (1) the subgroups defined by T, N, and M that make up a given group within a grouping scheme have similar survival rates (hazard consistency); (2) the survival rates differ among the groups (hazard discrimination); (3) the prediction of cure is high (outcome prediction); and (4) the distribution of patients among the groups is balanced. We identified or derived a measure for each criterion, and the findings were summarized by use of a scoring system. The range of scores was from 0 (best) to 7 (worst). The data are population based from a prospectively gathered series in Southern Norway, with 556 patients diagnosed from 1983 through 1995. Clinical stage assignment was used, and the outcome of interest was cause-specific survival. RESULTS: Summary scores across the eight schemes ranged from 1.66 for TANIS-3 to 6.50 for UICC/AJCC-5. The TANIS-7 staging scheme performed best on the hazard consistency criterion. The Kiricuta scheme performed best on the hazard discrimination criterion. Synderman predicted outcome best overall and Berg produced the most balanced distribution of cases among its groups. CONCLUSIONS: UICC/AJCC stage groupings were defined without empirical investigation. When tested, this scheme did not perform as well as any of seven empirically derived schemes we evaluated. Our results suggest that the usefulness of the TNM system could be enhanced by optimizing the design of stage groupings through empirical investigation.
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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) | 0.001 | 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 it