Understanding International Variation in Cancer-Specific 'Access to Diagnostics' Data and Steps Toward Cohesive Cancer Intelligence Frameworks: An International Cancer Benchmarking Partnership (ICBP) Study
Notice bibliographique
Résumé
Background: Robust and accurate data underpins cancer research, planning, control and comparisons; it shapes the policies and structures of health systems internationally. Access to diagnostics is crucial for timely cancer diagnosis and treatment planning as previous evidence has shown that delays in diagnosis can impact cancer outcomes. It is possible that differences in cancer outcomes internationally are a consequence of differing levels of access to diagnostic tests. By better understanding variation in this access, this relationship can be further explored. However, diagnostic data availability is not currently well documented. Aim: The primary goal of this exercise was to identify already existing routine or national datasets exploring 'access' variables relating to diagnostics for imaging and endoscopy tests. These access variables included capacity, use, workforce, location and financial factors, and where possible specific to the cancer population. Secondly, to address what high-income countries need to improve to fulfill the existing criteria for 'cancer intelligence frameworks', such as those set out by the National Health Service in England. Methods: Mixed methods including online searches and discussion with local contacts were used to explore key diagnostic data variables across the seven participating countries of ICBP phase 2 (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK). Results: Gaps and inconsistencies in diagnostics data were identified in each country. These key issues make comparisons within and between countries challenging: inconsistent definitions, collection at different levels within a health system, and queries about the coverage, reliability, and linkage of data (especially for cancer) were raised. The usage and allocation of workforce is also poorly documented, and a lack of appropriate infrastructure raised as a key barrier to better collection of data. Currently, most countries do not have a centralised data collection organization, and there are no international or standardized definitions for the diagnostic data that should be collected and could be compared. Conclusion: Health data are disparately collected internationally, with little diagnostics data that can be linked to cancer populations. The data sources and gaps identified add weight to existing efforts to improve data collections and health service planning. International agreement on the key performance indicators, their definitions and how best to organize collected data are required to address gaps and enable robust comparisons. These definitions and an understanding of best practice will be useful for middle- and low-income countries who want to develop or start collecting cancer-specific data. Existing 'cancer intelligence' frameworks could be adapted for international use, but rely on the agreement and adoption of standardized definitions and metrics for the cancer population.
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 | 0,166 | 0,210 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,013 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».