Cross-Community Collaboration and Data Collection to Optimize Patient Care in Hemolytic Anemias
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.376 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".