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CRISPR-Cas9 Gene Editing for Sickle Cell Disease and β-Thalassemia

2021· article· en· W3175458943 on OpenAlexaff
Haydar Frangoul, David Altshuler, Maria Domenica Cappellini, Yi-Shan Chen, Jennifer Domm, Brenda K. Eustace, Juergen Foell, Josu de la Fuente, Stephan A. Grupp, Rupert Handgretinger, Tony W. Ho, Antonis Kattamis, Andrew Kernytsky, Julie Lekstrom-Himes, Amanda M. Li, Franco Locatelli, Markus Y. Mapara, Mariane de Montalembert, Damiano Rondelli, Akshay Sharma, Sujit Sheth, Sandeep Soni, Martin H. Steinberg, Donna A. Wall, Angela Yen, Selim Corbacioglu

Bibliographic record

VenueObstetrical & Gynecological Survey · 2021
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineThalassemiaDiseaseHemoglobinopathyBlood transfusionCRISPRImmunologyGeneInternal medicineGeneticsBiology

Abstract

fetched live from OpenAlex

(Abstracted from N Engl J Med 2021;384:252–260) Transfusion-dependent β-thalassemia (TDT) and sickle cell disease (SCD) are the most common inherited hematologic disorders, affecting approximately 60,000 and 300,000 patients worldwide, respectively. Current therapies, including red blood cell (RBC) transfusion and iron chelation in TDT and transfusion, pain management, and hydroxyurea in SCD, help to manage the disorders but do not address the underlying cause.

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)0.000
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.279
Teacher spread0.250 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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