Cross-Community Collaboration and Data Collection to Optimize Patient Care in Hemolytic Anemias
Notice bibliographique
Résumé
Background: Patient engagement is becoming increasingly important for all facets of healthcare, from drug development and approval, to ensuring equitable access and the delivery of care. It is imperative to bring the voice of those impacted by the actual disease into these processes: this is particularly true for rare diseases, such as hereditary hemolytic anemias (HHAs), where the disease burden is often high and coupled with a poor quality of life and complex treatment requirements. The Red Cell Revolution™ (RCR) advisory council arose organically as part of an Agios Pharmaceuticals-sponsored discovery process, during which a range of stakeholders including healthcare providers (HCPs), advocates, patients, and company leaders offered perspectives on how to optimize engagement with the disease communities. Insights uncovered as part of this process demonstrated that there are unifying health concerns and needs among those impacted by pyruvate kinase (PK) deficiency, sickle cell disease (SCD), and thalassemia, and that there is an opportunity to explore new patient-advocacy research, supported by the creation of a unique multi-stakeholder council. Objective: To apply a multi-stakeholder, rigorous patient-advocacy data collection approach to understand the unmet needs of patients, caregivers, and HCPs for three HHAs: PK deficiency, SCD, and thalassemia. Methods: The RCR (supported by Agios Pharmaceuticals) was established across three allied disease areas, with representation from patients (N=5), caregivers (N=1), advocates (N=3), clinicians (N=8), and Agios representatives (N=5) impacted by HHA. The patient advocacy research method commenced with a survey shared with all RCR members, which curated both qualitative and quantitative insights from the group. These insights subsequently underwent cluster analysis to determine the shared concerns experienced by those impacted by HHA. The data were also subjected to linguistic analysis whereby terminology used to describe the experiences of participants was ranked according to frequency of use to reveal the most prominent concerns. The results of these analyses were distilled into an agreed group vision by the RCR, and a specific research strategy was aligned on for the RCR to pursue. Results: RCR members provided detailed answers about unmet needs across three categories: 1) impact of disease; 2) local and regional community needs; and 3) international community needs. When applied, the analysis uncovered 12 common concerns expressed by all participants across the three disease areas. These are outlined in Table 1. These insights were distilled into four key topic areas: 1) emotional and physical fatigue, 2) timely care, 3) transition from pediatric to adult care, and 4) access disparities, which were then ranked according to four parameters, namely whether it was: 1) common across all three disease areas, 2) global in scope, 3) high potential for lasting impact, and 4) revolutionary (involving or causing a complete or dramatic change). This produced alignment on one key priority: emotional and physical fatigue. Conclusion: The commonality analysis deployed here demonstrates the ability of a multi-stakeholder council to determine priority areas for research to address unmet needs. The RCR will conduct an in-depth evidence audit of the existing research in this field to identify key knowledge gaps, enabling the design of a study that will answer outstanding research questions. This study will generate patient experience data with the potential to inform both the clinical setting and drug development. As a next step, the RCR is developing a patient advocacy research study to collect the necessary evidence to better understand fatigue and its impact on psychosocial quality of life measures, such as feelings of guilt.
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 | 0,376 | 0,366 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,008 | 0,006 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,005 | 0,028 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».