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Record W2051291677 · doi:10.1002/ajmg.a.36111

Bone marrow transplantation in Schimke immuno‐osseous dysplasia

2013· review· en· W2051291677 on OpenAlexafffund
Alireza Baradaran‐Heravi, J. Lange, Yumi Asakura, Pierre Cochat, Laura Massella, Cornelius F. Boerkoel

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2013
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesHospital for Sick Children
KeywordsMedicineDysplasiaTransplantationBone marrow transplantationWiskott–Aldrich syndromeBone marrowGenetic disorderImmunologyInternal medicineDiseaseBiologyGenetics

Abstract

fetched live from OpenAlex

Schimke immuno-osseous dysplasia (SIOD, OMIM 242900) is a rare autosomal recessive multisystem childhood disorder characterized by short stature, renal failure, T-cell immunodeficiency, and hypersensitivity to genotoxic agents. SIOD is associated with biallelic mutations in SMARCAL1 (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin, subfamily a-like 1), which encodes a DNA stress response enzyme with annealing helicase activity. Two features of SIOD causing much morbidity and mortality are bone marrow failure and T-cell deficiency with the consequent opportunistic infections. To address the safety and efficacy of bone marrow transplantation (BMT) in SIOD, we reviewed the outcomes of the only five SIOD patients known to us in whom bone marrow or hematopoietic stem cell transplantation has been attempted. We find that only one patient survived the transplantation procedure and that the existing indicators of a good prognosis for bone marrow transplantation were not predictive in this small cohort. Given these observations, we also discuss some considerations for the poor outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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
GenreReview

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

Citations29
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part ASame topicImmunodeficiency and Autoimmune DisordersFrench-language works237,207