Global Consultation on Cancer Staging (GCCS): An International Survey Evaluating the Understanding and Use of the Cancer Stage Classification Terminology
Bibliographic record
Abstract
Background: The UICC TNM classification of malignant tumors is an internationally agreed standard to describe and categorizes cancer stage. It allows for collecting both clinical and pathologic TNM information where available. However, complex changes have emerged, such as an explosive growth in knowledge about cancer in particular regarding other prognostic factors for outcome resulting in confusion regarding the uniform taxonomy of cancer stage around the globe. Aim: To evaluate the understanding and use of the cancer stage classification terminology. Methods: The GCCS Staging Working Group (under the UICC TNM Committee) designed a survey to appreciate the current status quo in understanding and application of TNM classification terminology on cancer within common users of TNM classification. An online survey was developed and piloted within 10 TNM users. The survey was refined for clarification of language and content. The finalized survey was sent to common TNM users across the world. The survey comprised 35 question using Likert Scale on 4 critical domains. A reminder was sent after 2 weeks of initial e-mail for online survey. The final responses were summarized using descriptive analysis method. Results: A total of 376 surveys invites were sent and 136 (36.2%) full replies received. From 136 responders, age (half < 56 years old) and gender was balanced. North America and Europe were the most commonly represented regions. About 65% were clinician and 40% reported to work in academic centers. Reporting on the study aims: (1) more than 80% very frequently use TNM information to determine a patient prognosis and/or treatment, (2) 85% state that anatomic disease extent should be reported separately from other factors and only half of the responders believe that use of TNM staging terminology is consistent or uniform. In addition, 81% agree that tumor markers have been shown to provide valid additional impact (in addition to extent of disease) on prognosis. However, the majority (55% overall or 70% if clinicians only) don't believe that an overarching framework for classifying genetic makers is available. Finally, there was no consensus on how anatomic extent of disease and other prognostic factors should be combined. Conclusion: Our results show that majority use TNM information to determine prognosis and treatment and that anatomic extent of disease should remain reported separately. Tumor markers have been shown to provide valid additional impact on prognosis. However, there is no consensus on how anatomic extent of disease and other prognostic factors should be combined. In addition, 50% believe that the application of the TNM staging terminology is not consistent or uniform in the literature.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".