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Record W3186750869 · doi:10.3389/fneur.2021.688362

Cerebral Autoregulation in Subarachnoid Hemorrhage

2021· review· en· W3186750869 on OpenAlexafffund
Darcy Lidington, Hoyee Wan, Steffen‐Sebastian Bolz

Bibliographic record

VenueFrontiers in Neurology · 2021
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsHeart and Stroke FoundationTed Rogers Centre for Heart ResearchUniversity of Toronto
FundersUniversity of TorontoBrain Aneurysm FoundationHeart and Stroke Foundation of Canada
KeywordsSubarachnoid hemorrhageAutoregulationMedicineCerebral autoregulationCerebral perfusion pressureCerebral blood flowIschemiaCardiologyStroke (engine)Intensive care medicineAnesthesiaInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Subarachnoid hemorrhage (SAH) is a devastating stroke subtype with a high rate of mortality and morbidity. The poor clinical outcome can be attributed to the biphasic course of the disease: even if the patient survives the initial bleeding emergency, delayed cerebral ischemia (DCI) frequently follows within 2 weeks time and levies additional serious brain injury. Current therapeutic interventions do not specifically target the microvascular dysfunction underlying the ischemic event and as a consequence, provide only modest improvement in clinical outcome. SAH perturbs an extensive number of microvascular processes, including the “automated” control of cerebral perfusion, termed “ cerebral autoregulation .” Recent evidence suggests that disrupted cerebral autoregulation is an important aspect of SAH-induced brain injury. This review presents the key clinical aspects of cerebral autoregulation and its disruption in SAH: it provides a mechanistic overview of cerebral autoregulation, describes current clinical methods for measuring autoregulation in SAH patients and reviews current and emerging therapeutic options for SAH patients. Recent advancements should fuel optimism that microvascular dysfunction and cerebral autoregulation can be rectified in SAH patients.

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 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 score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.027
GPT teacher head0.293
Teacher spread0.266 · 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

Citations77
Published2021
Admission routes2
Has abstractyes

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