Examining factors affecting clinical trial enrolment in the Clinical Trials Navigator program.
Notice bibliographique
Résumé
e23143 Background: Oncology clinical trial accrual rates are estimated to be around 5%, despite the potential benefits patients could receive on a clinical trial. Many factors contribute to whether a patient is successfully enrolled onto a clinical trial. The Clinical Trials Navigator (CTN) Program helps oncology patients identify clinical trials. We analyzed program and patient characteristics to determine features of successful enrollment. Methods: A retrospective study was conducted. From March 2019 to April 2024, 411 records from the CTN program were analyzed. Of the 411, 73 were referred to a clinical trial. 14 of the 73 were enrolled onto a trial. The characteristics evaluated for the 14 enrolled and the 59 non-enrolled patients were: age, distance from home center to clinical trial site, CTN processing time, and time of initial CTN application to death. For the non-enrolled, the reason for non-enrollment was recorded. For the enrolled, the type of trial, trial phase and discipline were recorded. All comparative values were analyzed using a Welch’s T-test. Results: Comparison of the data between the enrolled and non-enrolled revealed that the average age was similar between both groups with the enrolled being 61 years, and non-enrolled being 57 years ( p = 0.154). The mean distance from home center to clinical trial site was 332.9 kilometers (km) for enrolled and 407.6 km for non-enrolled ( p = 0.152). The CTN processing time for the enrolled group had a mean time of 4.1 days and the non-enrolled had a mean time of 12.5 days ( p = 0.002). The time of initial CTN application to death for the enrolled group had a mean of 17.4 months and the non-enrolled had a mean 7.9 months. ( p = 0.0051). For the non-enrolled group, the reason for non-enrollment was centre specific (60.1%) or patient specific (39.9%). The centre specific reasons included: non-eligibility (45%), trial no longer available (31%), centre declined (16%), COVID-19 delays (5%), not accepting patients (3%). The patient specific reasons included, the patient: passed away during the process (48%), sought alternative treatment (19%), declined (24%), lost to follow up (9%). For the enrolled group, the trial types were interventional (71%) and next generation sequencing (NGS) (29%). Enrollment of patients by phase of trial were as followed: three phase I (21.4%), two phase I/II (14.3%), three phase II (21.4%), one phase II/III (7.1%), one phase III (7.1%), and four NGS (28.6%). Conclusions: These findings highlight some of the unique barriers and opportunities for patients in patient-centered clinical trials enrollment. The importance of the efficiency of the CTN is highlighted. Almost one-third of patients were enrolled in phase I or phase I/II trials, demonstrating patient’s willingness to travel for early phase trials. Early referral in the patient journey, inclusion of all phases of trials and efficient patient processing will lead to higher clinical trials accrual.
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 | 0,024 | 0,126 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».