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Record W4317781122 · doi:10.1002/ajmg.c.32032

Gene‐targeted therapies: Towards equitable development, diagnosis, and access

2023· article· en· W4317781122 on OpenAlexaff
Amy Gaviglio, Mark W. Skinner, Lily J. Lou, Richard S. Finkel, Erika F. Augustine, Aaron J. Goldenberg

Bibliographic record

VenueAmerican Journal of Medical Genetics Part C Seminars in Medical Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEquity (law)ReimbursementDiseasePsychological interventionMedicineRare diseaseHealth careIntensive care medicineBusinessPolitical scienceEconomic growthPathologyPsychiatryEconomics

Abstract

fetched live from OpenAlex

Genomic and gene-targeted therapies hold great promise in addressing the global issue of rare diseases. To achieve this promise, however, it is critical the twin goals of equity in access to testing and diagnosis, and equity in access to therapy be considered early in the life cycle of development and implementation. Rare disease researchers and clinicians must simultaneously recognize the life-altering potential of early diagnosis and administration of gene-targeted therapeutics while acknowledging that not everyone who experiences a rare disease and needs these therapies will be able to afford or access them. Achieving equity in the development of and access to gene-targeted therapies will not only require innovations in research, clinical, regulatory, and reimbursement frameworks, but will also necessitate increased attention to the ethical, legal, and social implications when establishing research paradigms and the translation of research results into novel interventions for rare genetic diseases. This article highlights and discusses the growing importance and recognition of health equity across the spectrum of rare disease research and care delivery.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.362
Teacher spread0.343 · 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 teacher head, not a consensus.

Study designOther design
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".

Quick stats

Citations20
Published2023
Admission routes1
Has abstractyes

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