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Record W3128891271 · doi:10.2217/frd-2020-0006

Transfusion of Modified Blood Components for the Treatment of Autoimmune Hemolytic Anemia: A Network Meta-Analysis

2021· article· en· W3128891271 on OpenAlexaff
Jiawen Deng, Fangwen Zhou, Chi Yi Wong, Elena Zheng, Emma Huang

Bibliographic record

VenueFuture Rare Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsMedicineIncidence (geometry)Autoimmune hemolytic anemiaAdverse effectAnemiaRandomized controlled trialInternal medicineImmunologyHemoglobinHemolytic anemiaMeta-analysis

Abstract

fetched live from OpenAlex

Aim: To evaluate the efficacy and safety of transfusing leukoreduced red blood cells (LRBCs) and/or washed RBCs (WRBCs) in treating adult patients with autoimmune hemolytic anemia. Materials & methods: Randomized control trials were included. Outcomes were response incidence, hematological parameters (HPs) and adverse event (AE) incidence. Results: 16 randomized control trials (n = 1061) were included. LRBC+WRBC yielded increased response incidence and improvements in HP compared with suspended RBCs (SRBCs). WRBCs improved HP while LRBCs only improved RBC count and total bilirubin. LRBCs and WRBCs both reduce AE incidences compared with SRBCs. Conclusion: Modified blood components may achieve increased response incidence and/or HP, as well as decreased AE compared with SRBCs.

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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.041
GPT teacher head0.274
Teacher spread0.233 · 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 designMeta-analysis
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

Citations5
Published2021
Admission routes1
Has abstractyes

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