Collection and Reporting of National Cancer Stage at Diagnosis Data in Australia (STaR Project)
Notice bibliographique
Résumé
Background: Stage at diagnosis is an important prognostic factor for cancer, providing contextual information for interpreting population health indicators such as mortality from cancer and cancer survival. Australian population-based cancer registries (PBCRs) routinely collect information on cancer incidence and mortality. The need for high quality, comprehensive national data on stage at diagnosis to supplement these data are widely recognized in Australia. The collection and dissemination of quality national stage data will enhance the: • ability to better monitor cancer outcomes, inform cancer control policy; • understand variations across different populations; and • identify where further research and targeted strategies may be required to improve cancer outcomes. Linking data on cancer stage at diagnosis with other administrative cancer data will also allow for a better understanding of the relationship between stage at diagnosis, treatments received, patterns of cancer recurrence, and survival outcomes. Aim: To strengthen national data capacity by collecting and reporting cancer stage at diagnosis for Cancer Australia's Stage, Treatment and Recurrence (STaR) project. Methods: Working with state and territory population-based cancer registries (PBCRs) and the Australian Pediatric Cancer Registry, Cancer Australia supported the development and testing of Business Rules for the collection of national cancer stage at diagnosis for: • The top 5 incident cancers based on the Tumor, Node, and Metastasis (TNM) staging system. These rules were endorsed by the Australasian Association of Cancer Registries (AACR) as a national standard in May 2016; and • Childhood cancers, with a separate set of Business Rules for 16 childhood cancer types based on the Toronto Pediatric Cancer Stage Guidelines. These rules were supported by the AACR as a national standard. Results: Using the AACR-endorsed Business Rules, comprehensive national cancer stage at diagnosis data for the top 5 incident cancers (for 2011) have been collected in Australia for the first time. Over 90% of incidence cases were able to be assigned a value for registry-derived (RD) stage at diagnosis for melanoma (97%), prostate (97%), and female breast (94%) cancers. Lower staging completeness was found for colorectal cancers (88%), and for lung cancers (72%). Business Rules for the collection of stage at diagnosis data for pediatric cancers have also been developed; 93% of sample cases diagnosed in the period 2006-2010 were able to be staged, ranging from 84% for nonrhabdomyosarcoma to 100% for hepatoblastoma. Conclusion: The Business Rules enabled the uniform collection of cancer stage at diagnosis data for the first time in Australia. The collection of these data will allow for the linkage of stage at diagnosis to other sources of information, including patterns of treatments applied, and enable reporting of survival and recurrence outcomes by stage.
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 | 0,049 | 0,097 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
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 ».