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Record W2074238787 · doi:10.1016/j.bbmt.2012.11.218

Immune Reconstitution Following Reduced Intensity Stem Cell Transplantation for Non-Malignant Disorders in Children

2013· article· en· W2074238787 on OpenAlexaff
Jeffrey J. Bednarski, Catherine T. Le, Lisa Murray, Robert J. Hayashi, Lolie C. Yu, Jignesh Dalal, Naynesh Kamani, David A. Jacobsohn, Michael A. Pulsipher, Aleksandra Petrović, Ka Wah Chan, Michael Grimley, Paul R. Haut, Roberta H. Adams, Dorothea Douglas, Sonali Chaudhury, Andrew L. Gilman, Jennifer Jaroscak, Martin Andreánsky, Kirk R. Schultz, Jennifer Willert, Shalini Shenoy

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2013
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsAlemtuzumabMedicineStem cellTransplantationImmune systemRegimenImmunologyConditioning regimenOncologyInternal medicineHematopoietic stem cell transplantation

Abstract

fetched live from OpenAlex

Myeloablative stem cell transplants (SCT) for nonmalignant disorders (NMD) are complicated by early and late treatment-related toxicities. We used a novel reduced intensity conditioning (RIC) regimen with early administration of alemtuzumab to achieve donor engraftment with lower toxicities in NMD. Delayed immune reconstitution (IR) and severe/fatal late infections have been previously described with RIC using alemtuzumab peri-SCT. Early administration in our protocol is designed to selectively deplete host immunity with minimal effects on post-transplant IR.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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