Improving the TNM classification: Findings from a 10-year continuous literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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