Data‐driven optimization of version 9 American Joint Committee on Cancer staging system for anal cancer
Bibliographic record
Abstract
INTRODUCTION: The American Joint Committee on Cancer (AJCC) staging system undergoes periodic revisions to maintain contemporary survival outcomes related to stage. Recently, the AJCC has developed a novel, systematic approach incorporating survival data to refine stage groupings. The objective of this study was to demonstrate data-driven optimization of the version 9 AJCC staging system for anal cancer assessed through a defined validation approach. METHODS: The National Cancer Database was queried for patients diagnosed with anal cancer in 2012 through 2017. Kaplan-Meier methods analyzed 5-year survival by individual clinical T category, N category, M category, and overall stage. Cox proportional hazards models validated overall survival of the revised TNM stage groupings. RESULTS: Overall, 24,328 cases of anal cancer were included. Evaluation of the 8th edition AJCC stage groups demonstrated a lack of hierarchical prognostic order. Survival at 5 years for stage I was 84.4%, 77.4% for stage IIA, and 63.7% for stage IIB; however, stage IIIA disease demonstrated a 73.0% survival, followed by 58.4% for stage IIIB, 59.9% for stage IIIC, and 22.5% for stage IV (p <.001). Thus, stage IIB was redefined as T1-2N1M0, whereas Stage IIIA was redefined as T3N0-1M0. Reevaluation of 5-year survival based on data-informed stage groupings now demonstrates hierarchical prognostic order and validated via Cox proportional hazards models. CONCLUSION: The 8th edition AJCC survival data demonstrated a lack of hierarchical prognostic order and informed revised stage groupings in the version 9 AJCC staging system for anal cancer. Thus, a validated data-driven optimization approach can be implemented for staging revisions across all disease sites moving forward.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".