Refining Lung Cancer Screening Criteria in the Era of Value-Based Medicine
Notice bibliographique
Résumé
Lung cancer remains the leading cause of cancer mortality worldwide, and while mortality is gradually decreasing in high-income countries for most cancers, lung cancer mortality is not decreasing and is actually increasing in women [1].Moreover, effective treatment for advanced stages of lung cancer remains elusive, suggesting a great need for early detection, if indeed efforts to prevent onset of smoking and rapid cessation fail.In 2011, the National Lung Screening Trial (NLST) demonstrated that screening for lung cancer with three annual chest CT scans in smokers (or those who quit within 15 years) of at least 30 pack years, between 55-74 years old, reduced mortality by 20% compared to a single chest radiograph [2].As important as this finding was, in an era of excessive and rising health care costs, it is necessary to carefully assess cost-effectiveness and refine screening criteria to maximize value.In this issue [3], ten Haaf and colleagues applied microsimulation modeling to 576 different scenarios to determine the population-based cost effectiveness of lung cancer CT screening, using the Canadian health care system threshold ($50,000 Canadian dollars per life-year gained) as a benchmark.The optimal screening scenario thus identified included smokers (or those who quit <10 years prior) with a smoking history of at least 40 pack years to be screened annually from ages 55-75.They estimate that such a screening strategy would reduce mortality 9.05% compared to no screening at an incremental cost-effectiveness ratio of $41,136 Canadian dollars (US$33,825, in 2015) per life-year gained.While they estimate this strategy would not catch as many lung cancers as using the criteria from the NSLT trial would, they predict the optimal strategy would be more cost-effective and would reduce expected false positive screens and lung cancer overdiagnosis compared to the NSLT criteria.The future goal will be to increase efficacy without additional cost.The desire to eliminate cancer is, in part, at odds with the move to value-based medicine.Value-defined as outcomes relative to cost-cannot be ignored, as health care occupies a large and increasing proportion of most nations' gross domestic product, particularly the United States.This battle has been playing out for several tumor types.For example, the United States Preventive Services Task Force (USPSTF) recently suggested that screening mammography for breast cancer begin for the general population of women at age 50 [4], but the American Cancer Society [5] and other medical specialty organizations continue to press for earlier initiation of screening.Debate also rages as to the age at which screening should stop, given the limited life expectancy of elderly patients and that some cancers may show a different, less aggressive, biology in late life.The hard fact remains that when cost-effectiveness enters into the decision of whether to screen, some cancers will be missed that otherwise could have been caught early and cured.To maximize the benefit of screening strategies, studies such as the one in this issue are needed to
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 | 0,015 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».