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Record W2974456904 · doi:10.3324/haematol.2019.227561

Extensive multilineage analysis in patients with mixed chimerism after allogeneic transplantation for sickle cell disease: insight into hematopoiesis and engraftment thresholds for gene therapy

2019· article· en· W2974456904 on OpenAlexaff
Alessandra Magnani, Corinne Pondarré, Naïm Bouazza, Jérémy Magalon, Annarita Miccio, Emmanuelle Six, Cécile Roudaut, Cécile Arnaud, Annie Kamdem, Fabien Touzot, Aurélie Gabrion, Elisa Magrin, Chloé Couzin, Mathieu Fusaro, Isabelle André‐Schmutz, Jean‐Paul Vernant, Éliane Gluckman, Françoise Bernaudin, Dominique Bories, Marina Cavazzana

Bibliographic record

VenueHaematologica · 2019
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersAgence Nationale de la Recherche
KeywordsImmunologyHaematopoiesisHematopoietic stem cell transplantationTransplantation ChimeraTransplantationProgenitor cellStem cellBiologyDiseaseMedicineHematopoietic cellInternal medicineGenetics

Abstract

fetched live from OpenAlex

Although studies of mixed chimerism following hematopoietic stem cell transplantation in patients with sickle cell disease (SCD) may provide insights into the engraftment needed to correct the disease and into immunological reconstitution, an extensive multilineage analysis is lacking. We analyzed chimerism simultaneously in peripheral erythroid and granulomonocytic precursors/progenitors, highly purified B and T lymphocytes, monocytes, granulocytes and red blood cells (RBC). Thirty-four patients with mixed chimerism and ≥12 months of follow-up were included. A selective advantage of donor RBC and their progenitors/precursors led to full chimerism in mature RBC (despite partial engraftment of other lineages), and resulted in the clinical control of the disease. Six patients with donor chimerism

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations36
Published2019
Admission routes1
Has abstractyes

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