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Record W2790765990 · doi:10.1097/aco.0000000000000566

Transfusion practices in traumatic brain injury

2018· review· en· W2790765990 on OpenAlexaff
James East, Julien Viau-Lapointe, Victoria McCredie

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2018
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineTraumatic brain injuryIntensive care medicineAnemiaBlood transfusionObservational studySurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this review is to summarize the recent studies looking at the effects of anemia and red blood cell transfusion in critically-ill patients with traumatic brain injury (TBI), describe the transfusion practice variations observed worldwide, and outline the ongoing trials evaluating restrictive versus liberal transfusion strategies for TBI. RECENT FINDINGS: Anemia is common among critically-ill patients with TBI, it is also thought to exacerbate secondary brain injury, and is associated with an increased risk of poor outcome. Conversely, allogenic red blood cell transfusion carries its own risks and complications, and has been associated with worse outcomes. Globally, there are large reported differences in the hemoglobin threshold used for transfusion after TBI. Observational studies have shown differential results for improvements in cerebral oxygenation and metabolism after red blood cell transfusion in TBI. SUMMARY: Currently, there is insufficient evidence to make strong recommendations regarding which hemoglobin threshold to use as a transfusion trigger in critically-ill patients with TBI. There is also uncertainty whether the restrictive transfusion strategy used in general critical care can be extrapolated to acutely brain injured patients. Ultimately, the consequences of anemia-induced cerebral injury need to be weighed up against the risks and complications associated with red blood cell 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
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 head0.238
GPT teacher head0.471
Teacher spread0.234 · 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 designNot applicable
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

Citations57
Published2018
Admission routes1
Has abstractyes

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