Global Consultation on Cancer Staging: To Promote Consistent Understanding and Use of Cancer Stage Terminology
Bibliographic record
Abstract
Background and context: Although the TNM stage schema has been the traditional means to classify anatomic extent of disease, in recent years confusion and uncertainty have emerged which underpinned by lack of familiarity concerning underlying rules of staging and their application. In turn such lack of clarity has led increased risk of miscommunication regarding patient care, research, cancer surveillance, epidemiology and cancer control. The UICC TNM Committee has confirmed a lack of uniformity in the application of cancer stage and its rules. In addition to stage, numerous other factors influence the outcome of patients as relate to tumor characteristics, patient descriptors, and the environment where any treatment is administered. A particularly a frequent problem is mixing disease extent and biology which has promoted additional misunderstanding about the importance and relevance of different individual prognostic elements and to what degree biology vs disease burden contribute to outcome. Aim: To ensure uniformity of staging systems, rules and classifications, the TNM Committee developed a global consensus on cancer staging. Strategy/Tactics: A selected literature review of twelve high impact oncology journals was performed and results will be summarized. There was inconsistent understanding and use of cancer stage classification terminology evident in up to 20% of the literature. A survey was developed and found that only 12.5% of those surveyed thought that the application of the TNM staging terminology was consistent and uniform in the literature. Respondents believed that complete T, N and M data should be recorded in cancer registries, 71% considered that other predictive and prognostic factors should also be collected by central cancer registries but that anatomic disease extent should be collected as a separate variable (85%). The Global Consultation on Cancer Staging was held under the auspices of the Union for International Cancer Control (UICC) and Lancet Oncology with support from the United States (US) National Cancer Institute (NCI) and the US Centers for Disease Control and Prevention (CDC). Experts from these organizations and FIGO (Fédération Internationale de Gynécologie et d´Obstétrique), IACR (International Association of Cancer Registries), IARC (International Agency for Research in Cancer), and the ICCR (International Collaboration on Cancer Reporting) attended. Program/Policy process: The purpose of the staging classification was reaffirmed. Important issues about staging processes were annunciated, and inconsistencies in terminology and use were acknowledged. Definitions of frequently misused staging terms were clarified. What was learned: It was determined that methodologies need to be explored to identify and include necessary data elements relevant to personalized treatment. Selection of factors should particularly include attention to their inclusion in cancer registries where appropriate.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".