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Record W4385727676 · doi:10.1055/s-0043-1771470

Gastrointestinal Dysfunction in Stroke

2023· review· en· W4385727676 on OpenAlexaff
Heather Yong, Aravind Ganesh, Carlos R. Cámara-Lemarroy

Bibliographic record

VenueSeminars in Neurology · 2023
Typereview
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)Gut–brain axisDysbiosisGut floraDysphagiaGastrointestinal tractProinflammatory cytokineConstipationIschemic strokeCentral nervous systemInternal medicineBioinformaticsImmunologyInflammationSurgeryIschemia

Abstract

fetched live from OpenAlex

Gastrointestinal (GI) complications are seen in over 50% of ischemic stroke survivors; the most common complications are dysphagia, constipation, and GI bleeding. The bidirectional relationship of the gut-brain axis and stroke has recently gained traction, wherein stroke contributes to gut dysbiosis (alterations in the normal host intestinal microbiome) and gut dysbiosis perpetuates poor functional neurologic outcomes in stroke. It is postulated that the propagation of proinflammatory cells and gut metabolites (including trimethylamine N-oxide and short-chain fatty acids) from the GI tract to the central nervous system play a central role in gut-brain axis dysfunction. In this review, we discuss the known GI complications in acute ischemic stroke, our current knowledge from experimental stroke models for gut-brain axis dysfunction in stroke, and emerging therapeutics that target the gut-brain axis.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.099
GPT teacher head0.450
Teacher spread0.351 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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