Evaluating the prognostic contributions of TNM classifications and building novel staging schemes for middle ear squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: A universally acknowledged cancer staging system considering all aspects of the T-, N-, and M-classifications for middle ear squamous cell carcinoma (MESCC) remains absent, limiting the clinical management of MESCC patients. MATERIALS AND METHODS: A total of 214 MESCC patients were extracted from the SEER (the Surveillance, Epidemiology, and End Results) database between 1973 and 2016. The relationships between patient's characteristics and prognoses were analyzed by Kaplan-Meier and Cox proportional hazards regression models. Novel staging schemes for MESCC were designed by adjusted hazard ratio (AHR) modeling method according to the combinations of Stell's T-classification and the eighth AJCC N- and M-classifications, of which performances were evaluated based on five criteria: hazard consistency, hazard discrimination, explained variation, likelihood difference, and balance. RESULTS: T-classification was the most significant prognostic factor for MESCC patients in multivariable analysis (p = 0.021). The N- and M-classifications also had obvious prognostic effect but were not statistically significant by multivariate analysis due to the limited metastasis events. Three novel staging schemes (AHR-Ⅰ-Ⅲ models, different combination of T- and N-classifications) and ST (solely derived from Stell's T-classification) were developed, among which the AHR-Ⅰ staging scheme performed best. CONCLUSIONS: Tumor extension, quantified by Stell's T-classification, is the most significant prognostic factor for MESCC patients. However, our AHR-Ⅰ staging scheme, a comprehensive staging scheme that integrating T-, N-, and M-classifications, might be an optimal option for clinical practitioners to predict MESCC patients' prognosis and make proper clinical decisions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".