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Record W2089611642 · doi:10.1097/aco.0b013e32832ff4a2

Transfusion practice in neuroanesthesia

2009· review· en· W2089611642 on OpenAlexaff
Jonathan McEwen, KT Henrik Huttunen

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2009
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicinePerioperativeBlood managementIntensive care medicineBlood transfusionAnemiaBlood conservationSubarachnoid hemorrhageRed Blood Cell TransfusionAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Perioperative transfusion thresholds in the neurosurgical patient are undefined. Many neurosurgical procedures are associated with significant risk of bleeding. This review will summarize the current understanding of blood transfusion in the neurosurgical patient, as well as other blood component therapies and blood conservation strategies. RECENT FINDINGS: Transfusion of red blood cells has been demonstrated to improve cerebral oxygen delivery. Clinical studies on transfusion-related morbidity and mortality in the neurosurgical patient are limited. Recent findings in both subarachnoid hemorrhage and traumatic brain injured patients have shown worse outcomes in patients with anemia (Hb <9.0 g/dl) yet transfusion of red blood cells may not be associated with improved outcome. SUMMARY: Perioperative transfusion management for intracranial neurosurgical procedures presents the clinician with multiple challenges. Clinical evidence is sparse with view to an optimal hemoglobin level, yet anemia is known to be a predictor of poor outcome in many neurosurgical patients. Transfusion thresholds from other patient populations may not apply to this group and further prospective investigations are desperately needed. Until then, clinicians should focus on an individualized assessment of anemia tolerance, consider blood conservation strategies and understand the potential risks and benefits of blood transfusion.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.094
GPT teacher head0.419
Teacher spread0.325 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations17
Published2009
Admission routes1
Has abstractyes

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