Connecting clinical trials with patients using patient navigation: A scoping review.
Notice bibliographique
Résumé
e13560 Background: Patient navigation is a promising strategy to improve access to cancer care, but the evidence supporting its role in increasing access to cancer clinical trials has not been systematically evaluated. This scoping review aims to critically appraise, synthesize, and present the available evidence on the use of patient navigation to increase cancer clinical trial enrollment. Methods: Nine databases were searched for English peer‐reviewed articles from inception through December 21, 2023. Two independent researchers screened titles, abstracts, and full texts and extracted data using standardized forms. Results: Among the 23 included articles, 18 (78.3%) were observational studies, and only 5 (21.7%) were randomized trials. Fourteen (60.9%) were described as pilot/feasibility studies. Of the observational studies, 13 (56.5%) included a comparator group. Six (26.1%) studies were multi-institutional; 17 (73.9%) were single-center. Twenty-one (91.3%) studies were from the USA; 2 (8.7%) were from Canada. Thirteen (56.5%) focused on equity, all addressing racial/ethnic groups. Seven (30.4%) articles used patient navigation for clinical trials for all cancer types; 14 (60.9%) focused on specific cancers, with 12 (85.7%) primarily addressing breast cancer. Among 21 studies describing navigator qualifications, 4 (17.4%) required professional training (e.g., nurse, social worker), and 17 (73.9%) used community representatives. Education/training for navigators was described in 12 (52.2%) articles. The interventions used most frequently by navigators included education in 19 articles (82.6%) and care coordination in 17 articles (73.9%). Direct clinical trial referrals were unmentioned; logistical and financial assistance appeared in only 2 articles each (8.7%). Navigators in 7 (30.4%) studies directed patients to trials within and outside their center; 16 (69.6%) navigated patients only within their center. Five articles compared enrollment with and without navigation: 4 showed no improvement, and 1 reported improvement with navigation. Five other articles reported enrollment with navigation without a comparison group. Two studies limited to eligible patients reported 80.4% and 86% enrollment in a clinical trial with navigation. Three studies including all interested patients reported enrollment rates of 7%, 22%, and 22.5%. Conclusions: Evidence on patient navigation for cancer clinical trials is primarily from observational, pilot/feasibility, single-center studies in North America, with a focus on breast cancer. Furthermore, navigator training details are underreported and their interventions' scope is limited. Few studies have examined diverse equity groups. Future research should employ more rigorous designs to evaluate different patient navigation approaches and assess their impact on clinical trial enrollment across a wider range of cancers and patient populations.
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 | 0,092 | 0,338 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,020 | 0,026 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».