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Record W3001896555 · doi:10.1101/2020.01.15.20017657

Therapeutic use of blood products for the treatment of autoimmune hemolytic anemia: A network meta-analysis protocol

2020· preprint· en· W3001896555 on OpenAlexaff
Jiawen Deng

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHematocritAutoimmune hemolytic anemiaAnemiaMedicineHemoglobinRed blood cellAdverse effectReticulocyteMeta-analysisBlood transfusionHemolytic anemiaHypoxia (environmental)HemolysisImmunologyBilirubinInternal medicineOxygenBiologyChemistry

Abstract

fetched live from OpenAlex

ABSTRACT Autoimmune hemolytic anemia is a rare blood disorder that can result in anemic hypoxia. Currently, red blood cell (RBC) transfusion is the only effective method of treating this condition. We propose a network meta-analysis that investigates whether the use of different types of blood products (e.g. suspended RBC, leukoreduced RBC, washed RBC, etc.) can decrease adverse events, increase the rate of remission and improve lab results, including hemoglobin, RBC, reticulocyte counts, hematocrit and total bilirubin.

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)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation 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: Protocol · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.022
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.185
GPT teacher head0.343
Teacher spread0.158 · 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 designMeta-analysis
Domainnot available
GenreProtocol

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
Published2020
Admission routes1
Has abstractyes

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