A Coordinated Approach to Lung Cancer Screening in Canada
Notice bibliographique
Résumé
Background and context: Lung cancer screening with low-dose computed tomography is recommended by the Canadian Task Force on Preventive Health Care for individuals at high risk. While no organized lung cancer screening programs currently exist, several Canadian jurisdictions have begun to plan for program implementation with pilot programs, studies, or business cases. Aim: The Canadian Partnership Against Cancer (the Partnership) has supported lung cancer screening activities by initiating a series of projects to promote lung health in Canada. Strategy/Tactics: The Partnership responded to emerging evidence on lung cancer screening with the establishment of the Pan-Canadian Lung Cancer Screening Network (PLCSN) in 2012. The PLCSN brings together key stakeholders from across Canada to promote pan-Canadian collaboration and serves as a national platform for knowledge exchange. Program/Policy process: One of the first priorities of the PLCSN was the development of a consensus statement-based Lung Cancer Screening Framework for Canada in 2014. The Framework outlines key considerations for lung cancer screening programs, including screening eligibility, radiologic testing, pathology quality and reporting, diagnostic treatment and follow-up, and the inclusion of smoking cessation interventions. As the development of the Framework drew to completion, the second priority of the PLCSN was the development of national quality indicators for lung cancer screening. An initial set of ten national-level lung cancer screening quality indicators was developed for national reporting. Most recently, the PLCSN developed a list of five quality-related lung cancer screening questions that should be explored in advance of the widespread implementation of lung cancer screening programs. These considerations included eligibility, enrollment, smoking cessation, nodule management and the effect of lung cancer screening programs on projected lung cancer mortality. Other Partnership initiatives to promote lung health include health economic modeling for lung cancer screening and collecting data on evidence-based smoking cessation programs. Outcomes: These initiatives have aligned pan-Canadian lung cancer screening efforts to facilitate knowledge sharing and resource efficiency, standardization of data collection and reporting, and acceleration of lung cancer screening in Canada. As of January 2018, four provinces have completed business cases, one province has implemented a pilot study, and three trials are ongoing across the country. Partnership initiatives and resources were used by several jurisdictions to inform the development of lung health activities. What was learned: By initiating these activities in advance of organized lung cancer screening programs, the Partnership has contributed to the evidence base on best practices in lung cancer screening that will be necessary for successful program implementation.
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 | 0,013 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,014 | 0,004 |
| Communication savante | 0,008 | 0,002 |
| Science ouverte | 0,004 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».