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Enregistrement W2020297058 · doi:10.1097/jto.0000000000000283

Resource Utilization and Costs during the Initial Years of Lung Cancer Screening with Computed Tomography in Canada

2014· article· en· W2020297058 sur OpenAlexafffundabout
Sonya Cressman, Stephen Lam, Martin C. Tammemägi, William K. Evans, Natasha B. Leighl, Dean A. Regier, Corneliu Bolbocean, Frances A. Shepherd, Ming‐Sound Tsao, Daria Manos, Geoffrey Liu, Sukhinder Atkar-Khattra, Ian Cromwell, Michael R. Johnston, John R. Mayo, Annette McWilliams, Christian Couture, John C. English, John R. Goffin, David Hwang, Serge Puksa, Heidi Roberts, Alain Tremblay, Paul MacEachern, Paul Burrowes, Richard J. Finley, Glenwood Goss, Garth Nicholas, Jean M. Seely, Harmanjatinder S. Sekhon, John Yee, Kayvan Amjadi, Jean‐Claude Cutz, Diana N. Ionescu, Kazuhiro Yasufuku, Simon Martel, Kamyar Soghrati, Don D. Sin, Wan C. Tan, Stefan J. Urbanski, Zhaolin Xu, Stuart Peacock

Notice bibliographique

RevueJournal of Thoracic Oncology · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueLung Cancer Diagnosis and Treatment
Établissements canadiensSt. Paul's HospitalMemorial University of NewfoundlandFoothills Medical CentreUniversité LavalQueen Elizabeth II Health Sciences CentreInstitut universitaire de cardiologie et de pneumologie de QuébecBeatrice Hunter Cancer Research InstituteJuravinski Cancer CentreSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaDalhousie UniversityOttawa HospitalUniversity Health NetworkCancer Care OntarioVancouver General HospitalPrincess Margaret Cancer CentreBC Cancer AgencyMcMaster UniversityBrock UniversityCanadian Centre for Applied Research in Cancer Control
Organismes subventionnairesCanadian Cancer Society Research InstituteTerry Fox Research InstitutePfizerPrincess Margaret Cancer FoundationPartenariat Canadien Contre Le CancerCanadian Centre for Applied Research in Cancer ControlAstraZenecaEli Lilly and Company
Mots-clésMedicineComputed tomographyLung cancerResource (disambiguation)Resource useLung cancer screeningTomographyRadiologyIntensive care medicineMedical physicsEnvironmental resource managementOncology

Résumé

récupéré en direct d'OpenAlex

BackgroundIt is estimated that millions of North Americans would qualify for lung cancer screening and that billions of dollars of national health expenditures would be required to support population-based computed tomography lung cancer screening programs. The decision to implement such programs should be informed by data on resource utilization and costs.MethodsResource utilization data were collected prospectively from 2059 participants in the Pan-Canadian Early Detection of Lung Cancer Study using low-dose computed tomography (LDCT). Participants who had 2% or greater lung cancer risk over 3 years using a risk prediction tool were recruited from seven major cities across Canada. A cost analysis was conducted from the Canadian public payer's perspective for resources that were used for the screening and treatment of lung cancer in the initial years of the study.ResultsThe average per-person cost for screening individuals with LDCT was $453 (95% confidence interval [CI], $400–$505) for the initial 18-months of screening following a baseline scan. The screening costs were highly dependent on the detected lung nodule size, presence of cancer, screening intervention, and the screening center. The mean per-person cost of treating lung cancer with curative surgery was $33,344 (95% CI, $31,553–$34,935) over 2 years. This was lower than the cost of treating advanced-stage lung cancer with chemotherapy, radiotherapy, or supportive care alone, ($47,792; 95% CI, $43,254–$52,200; p = 0.061).ConclusionIn the Pan-Canadian study, the average cost to screen individuals with a high risk for developing lung cancer using LDCT and the average initial cost of curative intent treatment were lower than the average per-person cost of treating advanced stage lung cancer which infrequently results in a cure. It is estimated that millions of North Americans would qualify for lung cancer screening and that billions of dollars of national health expenditures would be required to support population-based computed tomography lung cancer screening programs. The decision to implement such programs should be informed by data on resource utilization and costs. Resource utilization data were collected prospectively from 2059 participants in the Pan-Canadian Early Detection of Lung Cancer Study using low-dose computed tomography (LDCT). Participants who had 2% or greater lung cancer risk over 3 years using a risk prediction tool were recruited from seven major cities across Canada. A cost analysis was conducted from the Canadian public payer's perspective for resources that were used for the screening and treatment of lung cancer in the initial years of the study. The average per-person cost for screening individuals with LDCT was $453 (95% confidence interval [CI], $400–$505) for the initial 18-months of screening following a baseline scan. The screening costs were highly dependent on the detected lung nodule size, presence of cancer, screening intervention, and the screening center. The mean per-person cost of treating lung cancer with curative surgery was $33,344 (95% CI, $31,553–$34,935) over 2 years. This was lower than the cost of treating advanced-stage lung cancer with chemotherapy, radiotherapy, or supportive care alone, ($47,792; 95% CI, $43,254–$52,200; p = 0.061). In the Pan-Canadian study, the average cost to screen individuals with a high risk for developing lung cancer using LDCT and the average initial cost of curative intent treatment were lower than the average per-person cost of treating advanced stage lung cancer which infrequently results in a cure.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,311
Score d'incertitude au seuil0,948

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,370
Écart entre enseignants0,352 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations50
Publié2014
Routes d'admission3
Résumé présentoui

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