Abstract 033: Achievement in Clinical Trials: Setting a New Standard Through the Successful Implementation of a Telehealth Enabled Clinical Core Lab for Neurologic Assessments
Notice bibliographique
Résumé
Background Telehealth has become a standard of care in almost every setting of clinical practice. In clinical Neurosciences/Neurovascular space it is widely used in prehospital field evaluations on the mobile stroke units, acute assessment and decision making in emergency department, ICUs and stroke unites as well as in outpatient clinical consultations and follow‐ups. Some of these applications allow patient assessment in their homes or other nonhospital based care facilities streamlining the access to clinical care. There is growing interest in applying similar telehealth concepts to clinical trials. FDA recently provided recommendation for implementing decentralized clinical trials where some of the activities occur at locations other than traditional clinical trial sites. HealthMerit/NeuroMerit is a novel platform of a Clinical Core Lab developed to complete the task of connecting with study subjects anywhere and performing standardized clinical assessments remotely. In this study, we performed a feasibility study to assess the ability of our Clinical Core lab with Remote Clinical Assessment Application. Methods We performed a prospective, single‐arm non‐randomized study of 17 healthy volunteers in 4 US sites to evaluate the effectiveness of using the NeuroMerit Clinical Core Lab interface to virtually complete the Montreal Cognitive Assessment (MoCA), NIH Stroke Scale (NIHSS), and modified Rankin Scale (mRS). The primary study endpoint was completion of all 3 neurological assessments remotely. Secondary endpoints included logistical parameters, including success of appointment scheduling, timeliness of establishing a remote connection, and success of data transfer. User satisfaction was also investigated via the Telehealth Useability Questionnaire (TUQ) and Computer System Use Ability Questionnaire (CSUQ). Results All 3 neurological assessments were successfully completed in all 17 subjects. Study sites were able to schedule a remote telehealth session within 72 hours in all subjects with availability (14/14 or 100%) and connected remotely within 10 minutes of the appointment time in most cases (82.4%). Mean assessment time was 19.8 minutes. Data transfer was successful in all cases. User satisfaction of the application was rated highly by both the subjects and study coordinators: the average response rate on the TUQ was 6.4 (scale of 1 through 7 with 7 being most favorable) and was 1.3 on the CSUQ (scale of 1 through 7 with 1 being most favorable). Conclusion Utilizing Clinical Core Lab remote clinical assessment application to conduct neurologic evaluations for clinical trials is feasible and offers high user satisfaction. Currently we are using the Clinical Core Lab telehealth platform in a pilot clinical trial in Republic of Georgia and Australia, followed by pivotal trial in the US. We plan to continue to test the platform on clinical trial participants to evaluate the real‐life impact on clinical trial workflow and patient satisfaction. We hope the implementation of Clinical Core Lab run telehealth clinical assessments holds promise to standardize neurologic evaluations and improve patient recruitment and retention in clinical trials.
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 | 0,323 | 0,176 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».