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Record W2143660008 · doi:10.1111/micc.12177

Cerebral Collaterals and Collateral Therapeutics for Acute Ischemic Stroke

2014· review· en· W2143660008 on OpenAlexafffund
Ian R. Winship

Bibliographic record

VenueMicrocirculation · 2014
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta Innovates - Health Solutions
KeywordsMedicineCardiologyCollateralStroke (engine)Collateral circulationInternal medicineAcute strokeBusinessTissue plasminogen activatorFinance

Abstract

fetched live from OpenAlex

Cerebral collaterals are vascular redundancies in the cerebral circulation that can partially maintain blood flow to ischemic tissue when primary conduits are blocked. After occlusion of a cerebral artery, anastomoses connecting the distal segments of the MCA with distal branches of the ACA and PCA (known as leptomeningeal or pial collaterals) allow for partially maintained blood flow in the ischemic penumbra and delay or prevent cell death. However, collateral circulation varies dramatically between individuals, and collateral extent is significant predictor of stroke severity and recanalization rate. Collateral therapeutics attempt to harness these vascular redundancies by enhancing blood flow through pial collaterals to reduce ischemia and brain damage after cerebral arterial occlusion. While therapies to enhance collateral flow remain relatively nascent neuroprotective strategies, experimental therapies including inhaled NO, transient suprarenal aortic occlusion, and electrical stimulation of the parasympathetic sphenopalatine ganglion show promise as collateral therapeutics with the potential to improve treatment of acute ischemic stroke.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.039
GPT teacher head0.346
Teacher spread0.308 · 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

Citations81
Published2014
Admission routes2
Has abstractyes

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