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Record W4407974813 · doi:10.1016/j.jtct.2025.01.754

Unlocking Gene Therapy for Sickle Cell Disease: Addressing the Resource Gap for Patients

2025· article· en· W4407974813 on OpenAlexaff
Michele Heffering‐Cardwell, Sara Barr, Susan E. Clarke, Jonas Mattsson, Rajat Kumar

Bibliographic record

VenueTransplantation and Cellular Therapy · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDiseaseGenetic enhancementMedicineResource (disambiguation)Intensive care medicineGeneInternal medicineGeneticsComputer scienceBiology

Abstract

fetched live from OpenAlex

Topic Significance & Study Purpose/Background/Rationale The approval of gene therapy affords another curative option for patients with severe sickle cell disease (SCD), apart from transplant. Patients with SCD aged 12 years or older, who have experienced a minimum of two vaso-occlusive episodes per year for two consecutive years, would be eligible according to Health Canada. In our center's experience with patients who have SCD we found that this population does not align with traditional care standards with regards to patient navigation, coordination, education, and care needs. Education resources available to patients regarding this treatment is sparse, leading to lack of understanding concerning the process, challenges, eligibility and suitability criteria. The purpose is to provide a dynamic educational resource to address the unique care needs for adults with SCD who are potentially eligible for gene therapy. Methods, Intervention, & Analysis To inform the development of practice standards and educational materials, the authors reviewed literature and existing resources (eg. N Engl J Med 2024; 390: 1649-62) to create electronic education modules tailored to the unique care needs of this patient population. Further, the authors partnered with patients who underwent allogeneic transplant for SCD to obtain feedback on their treatment experience to guide content development. This allowed for a deeper understanding of their distinct challenges and personal disease interpretation aiding in the development of structured education. Findings & Interpretation There is a significant gap in available education resources for this patient population particularly as it pertains to gene therapy; leading to lack of understanding about this treatment modality. Patients have expressed that they face additional stress as they navigate their care within a “cancer” center, without a “cancer” diagnosis. The education modules aim to address unique care needs, which include the following; a) treatment indications and eligibility criteria, b) red cell exchange, stem cell mobilization, collection, timelines, and challenges c) pre-treatment assessments for organ function, d) gene editing e) myeloablative conditioning, f) side effects and complications, g) unique socioeconomic challenges, h) fertility preservation, i) recovery timelines, and j) long-term follow-up. The modules aim to enhance patient understanding of the care trajectory, instill confidence in decision making, and enhance commitment to the rigorous treatment process. Discussion & Implications Development of the electronic education modules is a valuable resource to support knowledge gaps for patients, families, and their caregivers undergoing approved gene therapy for SCD. The modules aid in debunking myths and misconceptions around treatment options and care pathways to support informed decision making. Additionally, this will serve as an accessible resource to share with other centers.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.018
GPT teacher head0.294
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractno

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