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Record W2794271556 · doi:10.1097/mcc.0000000000000485

Is hemoglobin good for cerebral oxygenation and clinical outcome in acute brain injury?

2018· review· en· W2794271556 on OpenAlexafffund
Shane English, Lauralyn McIntyre

Bibliographic record

VenueCurrent Opinion in Critical Care · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHemoglobin structure and function
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsMedicineHemoglobinOxygenationAnemiaIschemiaOxygen deliveryPathophysiologyAnesthesiaIntensive care medicineInternal medicineOxygen

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this review is to highlight the role of hemoglobin in cerebral physiology and pathophysiology. We review the existing as well as recent evidence detailing the effects of red blood cell transfusion on cerebral oxygenation and clinical outcome. RECENT FINDINGS: Hemoglobin is a key component in oxygen delivery, and thus cerebral oxygenation. Higher hemoglobin levels and red blood cell transfusion are associated with higher cerebral oxygen delivery and decreased cerebral ischemic burden. Recent studies suggest that this may be associated with improved clinical outcomes. However, these results are limited to only a few, small studies and the results have not been consistent. Further studies are required. SUMMARY: Hemoglobin is important for cerebral oxygenation and strategies to minimize anemia should be undertaken. Although higher hemoglobin levels are associated with less cerebral ischemia and better clinical outcome, whether this remains true whenever red blood cell transfusion is used to achieve this result remains unclear.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.517
Teacher spread0.333 · 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 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

Citations10
Published2018
Admission routes2
Has abstractyes

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