Essential TNM: A Means to Collect Stage Data in Population-Based Registries in Low- and Middle-Income Countries
Bibliographic record
Abstract
Background and context: Cancer control requires knowledge of cancer incidence. Information on anatomic extent of disease (stage) at presentation significantly enhances incidence and mortality data in understanding the cancer burden. The most frequently used staging classification of cancer disease extent is the tumor, node, metastases (TNM). Population-based registries (PBCR) in low- and middle-income countries (LMIC) frequently have insufficient information to derive complete TNM data, either because of inability to perform the necessary evaluations or because of a lack of recorded information. Aim: To develop a simplified system of recording extent of disease to facilitate the collection of stage data by PBCR and enhance the utility of data to facilitate cancer control in LMICs. Strategy/Tactics: A working group with representatives from the UICC (Union for International Cancer Control), the IARC (International Agency for Cancer Research), IACR (International Association of Cancer Registries) and the NCI (National Cancer Institute) was formed and Essential TNM was developed. When the T, N, and M categories have not been recorded in the clinical records or if the complete data to determine the categories is unavailable, the cancer registrar can code extent of disease according to the Essential TNM scheme. Once a cancer registrar had identifies the presence of metastatic disease (M1) this is recorded and additional information is unnecessary to establish that stage of disease. If there is no metastatic disease the extent of nodal disease is recorded. In turn if there is no nodal disease the extent/size of the primary carcinoma is recorded. The extent of disease can be summarized in the following order: M, N and T. Program/Policy process: Diagrams and rules for combining Essential TNM elements into stage groups (I-IV) or to be expressed as “distant”, “regional” or “localized” if only the most limited data were available, were developed for breast, cervix, prostate and colon cancers and will be demonstrated. Once the schema were developed they were verified in Georgia (USA) and field tested in Ecuador, Malawi, Cote d'Ivoire and Zimbabwe. Outcomes: There was good agreement between the stage identified through Essential TNM and that within the Georgia State Registry. The field tests however identified three key issues: the underidentification of distant metastases, inaccurate the collection of lymph node data and improved training needs. In particular there was uncertainty in the identification of when lymph node involvement was considered to be distant metastatic or regional. In view of this, refinements to the schemas have been made to simplify the collection of nodal data. The schema have been updated to ensure compatibility with the 8th edition of TNM. Training programs are being developed and Essential TNM is being expanded. What was learned: Essential TNM can be used by LMIC PBCR to facilitate the collection of stage data. Further refinements and training are needed and are underway.
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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 | 0.089 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".