Improving the TNM classification: Findings from a 10-year continuous literature review
Bibliographic record
Abstract
The Union for International Cancer Control's (UICC) TNM classification is a globally accepted system to describe the anatomic extent of malignant tumors. Since its development seventy years ago, the TNM classification has undergone significant revisions to reflect the current understanding of extent of disease and its role in prognosis. To ensure that revisions are evidence-based, the UICC implemented a process for continuous improvement of the TNM classification that included a formalized system for submitting proposals for revisions directly to the UICC and an annual review of the scientific literature on staging that assessed, criticized or made suggestions for changes. The process involves review of the proposals and literature by a group of international, multidisciplinary Expert Panels. The process has been in place for 10 years and informed the development of the 7th edition of the TNM classification published in 2009. The purpose of this article is to provide a description of the annual literature review process, including the search strategy, article selection process and the roles and requirements of the Expert Panels in the review of the literature. Since 2002, 147 Expert Panel members in 11 cancer sites have reviewed over 770 articles. The results of the annual literature reviews, Expert Panel feedback and documentation and dissemination of results are described.
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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 | 0.099 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".