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Record W2810562568 · doi:10.1186/s13054-018-2089-7

Anemia prevalence and incidence and red blood cell transfusion practices in aneurysmal subarachnoid hemorrhage: results of a multicenter cohort study

2018· article· en· W2810562568 on OpenAlexafffund
Shane English, Michaël Chassé, Alexis F. Turgeon, François Lauzier, Donald Griesdale, Allan Garland, Dean Fergusson, Ryan Zarychanski, Carl van Walraven, Kaitlyn Montroy, Jennifer Ziegler, Raphael Dupont-Chouinard, Raphaëlle Carignan, Andy Dhaliwal, Ranjeeta Mallick, John Sinclair, Amélie Boutin, Giuseppe Pagliarello, Alan Tinmouth, Lauralyn McIntyre

Bibliographic record

VenueCritical Care · 2018
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of ReginaUniversity of OttawaOttawa HospitalUniversity of ManitobaUniversité LavalUniversité de MontréalUniversity of British Columbia
FundersUniversity of Ottawa
KeywordsMedicineSubarachnoid hemorrhageBlood transfusionRetrospective cohort studyAnemiaCohortIncidence (geometry)PediatricsRed Blood Cell TransfusionEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Whether a restrictive strategy for red blood cell (RBC) transfusion is applied to patients with aneurysmal subarachnoid hemorrhage (aSAH) is unclear. To inform the design and conduct of a future clinical trial, we sought to describe transfusion practices, hemoglobin (Hb) triggers, and predictors of RBC transfusion in patients with aSAH. METHODS: This is a retrospective cohort study of all consecutively admitted adult patients with aSAH at four tertiary care centers from January 1, 2012, to December 31, 2013. Patients were identified from hospital administrative discharge records and existing local aSAH databases. Data collection by trained abstractors included demographic data, aSAH characteristics, Hb and transfusion data, other major aSAH cointerventions, and outcomes using a pretested case report form with standardized procedures. Descriptive statistics were used to summarize data, and regression models were used to identify associations between anemia, transfusion, and other relevant predictors and outcome. RESULTS: A total of 527 patients met inclusion eligibility. Mean (±SD) age was 57 ± 13 years, and 357 patients (67.7%) were female. The median modified Fisher grade was 4 (IQR 3-4). Mean nadir Hb was 98 ± 20 g/L and occurred on median admission day 4 (IQR 2-11). RBC transfusion occurred in 100 patients (19.0%). Transfusion rates varied across centers (12.1-27.4%, p = 0.02). Patients received a median of 1 RBC unit (IQR 1-2) per transfusion episode and a median total of 2 units (IQR 1-4). Median pretransfusion Hb for first transfusion was 79 g/L (IQR 74-93) and did not vary substantially across centers (78-82 g/L, p = 0.37). Of patients with nadir Hb < 80 g/L, 66.3% received a transfusion compared with 2.0% with Hb nadir ≥ 100 g/L (p < 0.0001). Predictors of transfusion were history of oral anticoagulant use, anterior circulation aneurysm, neurosurgical clipping, and lower Hb. Controlling for numerous potential confounders, transfusion was not independently associated with poor outcome. CONCLUSIONS: We observed that moderate anemia remains very common early in admission following SAH. Only one-fifth of patients with SAH received RBC transfusions, mostly in cases of significant anemia (Hb < 80 g/L), and this did not appear to be associated with outcome.

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)0.003
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.311
Teacher spread0.294 · 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

Citations34
Published2018
Admission routes2
Has abstractyes

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