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Record W2075621870 · doi:10.1097/aco.0b013e32835721a5

Blood pressure management in stroke

2012· review· en· W2075621870 on OpenAlexaff
Anne L. Donovan, Alana M. Flexman, Adrian W. Gelb

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2012
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineStroke (engine)ThrombolysisAcute strokeBlood pressureIntensive care medicineDiseasePopulationClinical trialInternal medicineCardiologyTissue plasminogen activatorMyocardial infarction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Cerebrovascular disease is a common cause of death and disability worldwide. The current literature supports an association between blood pressure (BP) and patient outcome during acute stroke. This review will provide an overview of the evidence to guide BP management during acute stroke. RECENT FINDINGS: Hypotension and hypertension are correlated with poor outcome in acute ischemic stroke, but the effect of reducing or augmenting BP is unclear. In most cases, BP should be treated only when SBP is greater than 220 or greater than 180 in candidates for thrombolysis. There is a lack of evidence to support the choice of specific agents. Use of vasopressor drugs to treat hypotension in acute stroke should be limited to selective situations. In acute hemorrhagic stroke, SBP greater than 140 has been correlated with poor outcomes. Two recent studies report the safety and feasibility of early BP reduction in hemorrhagic stroke. SUMMARY: Both hypertension and hypotension are associated with worse outcomes during acute stroke; however, the optimal hemodynamic parameters are not clearly defined in this patient population. Despite active research, there is a lack of high-quality data guiding current BP management in stroke. Several trials currently underway may clarify the many existing questions on this topic.

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 score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.106
GPT teacher head0.388
Teacher spread0.282 · 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

Citations19
Published2012
Admission routes1
Has abstractyes

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