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Record W2053999497 · doi:10.1212/wnl.0000000000000265

Poststroke atrial fibrillation: Cause or consequence?

2014· review· en· W2053999497 on OpenAlexaff
Luciano A. Sposato, Patricia M. Riccio, Vladimir Hachinski

Bibliographic record

VenueNeurology · 2014
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsAtrial fibrillationMedicineStroke (engine)CardiologyInternal medicineInsular cortexAutonomic nervous systemInsulaNeuroscienceHeart ratePsychologyBlood pressure

Abstract

fetched live from OpenAlex

Poststroke atrial fibrillation (AF) represents up to 1 of 4 overall AF cases in acute ischemic stroke. Current guidelines recommend oral anticoagulation for every ischemic stroke patient in whom AF is diagnosed. However, in some cases, AF detected after acute ischemic stroke may be short-lasting and perhaps a nonrecurrent autonomic and inflammatory epiphenomena of stroke. The autonomic regulation of cardiac rhythm constitutes an integrated relay system. The highest level of control is exerted by the cerebral cortex, particularly the insula. The onset of AF may be associated with an imbalance of sympathetic and parasympathetic activity, a common consequence of insular infarctions. This autonomic imbalance and an interruption in the cerebral regulation of the intrinsic cardiac autonomic system constitute the most likely mechanisms responsible for the autonomic pathway. The role of inflammation in the genesis of AF within the first few days after ischemic stroke may occur through inflammatory mediators stimulating the intrinsic autonomic system and by direct damage to atrial myocardium. To what extent poststroke AF is the cause or a consequence remains uncertain.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
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 head0.155
GPT teacher head0.410
Teacher spread0.255 · 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

Citations121
Published2014
Admission routes1
Has abstractyes

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