Global Consultation on Cancer Staging: promoting consistent understanding and use
Bibliographic record
Abstract
Disease burden is the most important determinant of survival in patients with cancer. This domain, reflected by the cancer stage and codified using the tumour-node-metastasis (TNM) classification, is a fundamental determinant of prognosis. Accurate and consistent tumour classification is required for the development and use of treatment guidelines and to enable clinical research (including clinical trials), cancer surveillance and control. Furthermore, knowledge of the extent and stage of disease is frequently important in the context of translational studies. Attempts to include additional prognostic factors in staging classifications, in order to facilitate a more accurate determination of prognosis, are often made with a lack of knowledge and understanding and are one of the main causes of the inconsistent use of terms and definitions. This effect has resulted in uncertainty and confusion, thus limiting the utility of the TNM classification. In this Position paper, we provide a consensus on the optimal use and terminology for cancer staging that emerged from a consultation process involving representatives of several major international organizations involved in cancer classification. The consultation involved several steps: a focused literature review; a stakeholder survey; and a consultation meeting. This aim of this Position paper is to provide a consensus that should guide the use of staging terminology and secure the classification of anatomical disease extent as a distinct aspect of cancer classification. Attempts to incorporate additional criteria into the traditional tumour-node-metastasis staging classification have often resulted in inaccuracy and confusion in the use of terminology. In this Position paper, the authors provide guidance on the consistent use of the terminology relating to cancer staging.
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.
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 | 0.157 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".