Transitioning a randomized controlled trial to a digital registry – experience from the TAILOR-PCI digital follow-up study on onboarding, engagement and geofencing consent rate
Notice bibliographique
Résumé
Abstract Background/Introduction TAILOR-PCI is the largest cardiovascular genotype-based randomized trial (NCT#01742117) investigating whether genotype-guided selection of oral P2Y12 inhibitor therapy improves ischemic outcomes after percutaneous coronary intervention (PCI). The TAILOR-PCI Digital Sub-Study tests the feasibility of extending original follow-up of 1 year to 2 years using state-of-the-art digital solutions. Deep phenotyping acquired during a clinical trial can be leveraged by extending follow-up in an efficient and cost-effective manner using digital technology. Purpose Our objective is to describe onboarding and engagement of participants initially recruited in a large, pragmatic, international, multi-center clinical trial to a digital registry. Methods TAILOR-PCI participants, within 23 months of their index PCI, were invited by letters containing a URL to the Digital Sub-Study website (http://tailorpci.eurekaplatform.org). These invitations were followed by phone calls, if no response to the letter, to determine reason for non-participation. A NIH-funded direct-to-participant digital research platform (the Eureka Research Platform) was used to onboard, consent and enroll participants for the digital follow-up. Participants were asked to answer health-related surveys at fixed intervals using the Eureka mobile app and desktop platform. To capture hospitalizations, participants could enable geofencing to allow background location tracking, which triggered surveys if a hospitalization was detected. Result(s) Letters were mailed to 893 of 929 eligible participants across 22 sites in the United States and Canada leading to 226 homepage visits and 118 registrations. There were 107 consents (12.0% of invited; mean age: 66.4±9.0; 19 females [18%]): 47 (44%) participants consented after the letter, 36 (34%) consented after the 1st call and 24 (22%) consented after a 2nd call. Among those who consented, 100 were eligible (7 did not have a smartphone) 81 downloaded the study mobile app and 73 agreed for geofencing (Figure 1). Among the 722 invited participants who were surveyed, 354 declined participation: due to lack of time (146; 20.2%), lack of smartphone (125; 17.3%), difficulty understanding (41; 5.7%), concern about using smartphone (34; 4.7%), concern of data privacy (14; 1.9%), concerns of location tracking (6; 0.8%) and other reasons (57; 7.9%). Conclusion Extended follow-up of a clinical trial using a digital platform is feasible but uptake in this study population was limited largely due to lack of time or a smartphone among participants. Based on data from other digital studies, uptake may also have been limited since digital follow-up consent was not incorporated at the time of consent for the main trial. Figure 1. Onboarding of the digital substudy Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Institute of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI)
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 | 0,117 | 0,169 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,003 |
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 ».