Essential TNM: A Means to Collect Stage Data in Population-Based Registries in Low- and Middle-Income Countries
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,089 | 0,160 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».