Predictors of clinical trial enrollment and impact on outcome in children and adolescents with acute lymphoblastic leukemia: A population based study.
Notice bibliographique
Résumé
7031 Background: Outcomes in pediatric acute lymphoblastic leukemia (ALL) have shown remarkable improvements in large part due to sequential clinical trials. Concerns however persist around whether access to clinical trials is equitable. It is also unclear whether patient outcomes are improved simply by enrolling on a clinical trial. Our objective was to therefore determine which patient and disease-related factors are associated with enrollment, and whether enrollment was associated with clinical outcomes among children and adolescents with ALL in a single-payer health system in Ontario, Canada. Methods: We included all Ontario patients diagnosed with ALL between 0-18 years of age from 2002-2012 treated at a pediatric center, identified through a provincial pediatric cancer registry. Clinical trial availability was determined by whether each patient’s primary institution had an open frontline trial for which the patient was eligible at the time of their diagnosis, considering individual disease characteristics such as lineage, central nervous system (CNS) status and risk group. Demographic, disease, trial enrolment, and outcome data were obtained through chart abstraction. Logistic regression models determined factors associated with trial enrolment, while Cox proportional hazard models determined factors associated with event-free and overall survival (EFS, OS). Results: Of 858 patients, 693 (81%) were eligible for an open clinical trial at their time of diagnosis. 476 (69%) enrolled on a trial. In adjusted analyses, age > 15 years (odds ratio 0.4 vs. age 5-9, 95th confidence interval (95CI) 0.2-0.8; p = 0.01) and CNS3 disease (OR 0.38 vs. CNS1, 95CI 0.17-0.83; p = 0.01) were significantly associated with decreased likelihood of enrolment, while sex and neighborhood income quintile were not associated with enrolment. Adjusted for disease and demographic factors, clinical trial enrolment was not significantly associated with either EFS (hazard ratio (HR) 1.1, 95CI 0.7-1.7; p = 0.83) or OS (HR 1.3, 95CI 0.7-2.5; p = 0.44). Conclusions: The majority of patients with ALL eligible for available clinical trials at their time of diagnosis were enrolled. While no disparities in enrolment by income status were noted, adolescents were substantially less likely to participate in trials even within pediatric centers. Studies of mechanisms underlying this disparity are warranted in order to design and implement effective interventions targeting increased enrolment rates in this patient population. Our results however also suggest that clinical trial enrolment on its own is not associated with improved outcomes in the context of a single payer health system.
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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».