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Record W4399685270 · doi:10.1111/vox.13690

Mortality‐associated risk factors for transfusion‐associated circulatory overload

2024· article· en· W4399685270 on OpenAlexaff
Lipika Soni, Samia Saeed, Christine Cserti‐Gazdewich, Mark J. McVey

Bibliographic record

VenueVox Sanguinis · 2024
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsToronto General HospitalUniversity of TorontoToronto Metropolitan UniversityUniversity Health NetworkHospital for Sick Children
Fundersnot available
KeywordsMedicineInterquartile rangeMultivariate analysisMultivariate statisticsUnivariateEmergency medicineUnivariate analysisBlood transfusionCohortInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Respiratory transfusion reactions associate strongly with morbidity and mortality, and transfusion-associated circulatory overload (TACO) is the leading cause of reaction-related deaths. Risk factors for TACO include transfusion speed and volume and cardiorenal comorbidities. MATERIALS AND METHODS: An academic health network haemovigilance database was interrogated to assess variables associating with 371 cases of TACO and involved-visit outcomes, using univariate and multivariate regression analysis. RESULTS: TACO reactions over 11 years were reported in 179 males and 192 females, median age (interquartile range) 65 (53-75) years. In-hospital and 28-day mortality were 17.5% and 12.9%, respectively. In univariate regression modelling, male sex, injury severity grade, product volume administered, the use of platelets and intensive care admissions were each associated with in-hospital and 28-day mortality (p < 0.05). However, after multivariate regression analysis, only male sex in transfusion recipients independently associated with mortality (p < 0.05). CONCLUSION: In this cohort, male recipient sex and platelet administration were associated with TACO-involving admissions not ending in survival.

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 score0.273
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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