Adherence to Annual Lung Cancer Screening and Rates of Cancer Diagnosis
Notice bibliographique
Résumé
Importance: Adherence to annual lung cancer screening (LCS) is a proposed quality metric for LCS programs, but data linking annual adherence to lung cancer outcomes are lacking. Objective: To investigate annual LCS adherence rates across 2 subsequent LCS rounds among adults undergoing baseline LCS and examine the association of adherence with lung cancer diagnosis rates. Design, Setting, and Participants: This retrospective cohort study included adults aged 55 to 75 years who formerly or currently smoked and underwent baseline LCS between January 1, 2015, and December 31, 2018, across 5 US health care systems in the Population-Based Research to Optimize the Screening Process-Lung Consortium. Participants with missing Lung Computed Tomography Screening Reporting & Data System scores or a lung cancer diagnosis prior to LCS initiation were excluded. Data were analyzed from October 2023 to October 2024. Exposures: For negative baseline screening results, T1 and T2 screening adherence was defined as chest computed tomography (CT) between 10 and 18 months and 22 and 30 months after baseline, respectively. For positive baseline screening results, T1 and T2 adherence was defined as chest CT between 11 and 21 months and 28 and 36 months after baseline, respectively. Main Outcomes and Measures: The main outcomes were annual T1 and T2 LCS adherence rates and associations between T1 and T2 screening adherence; annual incident lung cancer diagnoses in rounds T0 (0-12 months after baseline), T1 (>12 to 24 months after baseline), and T2 (>24 to 36 months after baseline); and cancer stage distribution. Results: A total of 10 170 individuals received baseline LCS (median age, 65 years [IQR, 60-69 years]; 5415 [53.2%] male). During round T1, 6141 of 10 033 eligible patients (61.2% [95% CI, 60.2%-62.2%]) were adherent, and during round T2, 5028 of 9966 eligible patients (50.5% [95% CI, 49.5%-51.4%]) were adherent. T1 adherence was significantly associated with T2 adherence (adjusted relative risk, 2.40; 95% CI, 2.06-2.79). Across 36 months of follow-up, 279 patients (2.7%; 95% CI, 2.4%-3.1%) were diagnosed with lung cancer. Incident lung cancer diagnosis rates were 1.3% (95% CI, 1.1%-1.6%), 0.7% (95% CI, 0.5%-0.8%), and 0.8% (95% CI, 0.6%-0.9%) during rounds T0, T1, and T2, respectively. Lung cancer diagnosis rates were higher among individuals who were LCS adherent vs nonadherent during both rounds T1 (59 of 6141 [1.0%; 95% CI, 0.7%-1.2%] vs 8 of 3892 [0.2%; 95% CI, 0.1%-0.4%]; P < .001) and T2 (63 of 5028 [1.3%; 95% CI, 1.0%-1.6%] vs 12 of 4938 [0.2%; 95% CI, 0.1%-0.4%]; P < .001). A greater proportion of early-stage lung cancers were diagnosed among individuals adherent to screening at T2 compared with those who were not (46 of 63 [73.0%] vs 3 of 12 [25.0%]; P = .006). Conclusions and Relevance: In this multicenter cohort study of adults undergoing LCS, screening adherence was associated with increased overall and early-stage lung cancer detection rates; however, adherence decreased annually after baseline screening, suggesting that it is an important LCS quality metric.
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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».