A comparison of published head and neck stage groupings in carcinomas of the oral cavity
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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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".