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The participation of the sympathetic innervation of the gastrointestinal tract in disease states

2009· review· en· W2090930564 on OpenAlexafffund
Alan Lomax, Keith A. Sharkey, John B. Furness

Bibliographic record

VenueNeurogastroenterology & Motility · 2009
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversity of CalgaryHotchkiss Brain InstituteQueen's University
FundersCanadian Institutes of Health Research
KeywordsSympathetic nervous systemIleusImmune systemGastrointestinal tractInflammationPathologicalEnteric nervous systemDiabetes mellitusDiseaseAutonomic nervous systemMedicineGut floraNeuroscienceBiologyImmunologyInternal medicineEndocrinologyBlood pressureHeart rate

Abstract

fetched live from OpenAlex

Knowledge of neural circuits, neurotransmitters and receptors involved in the sympathetic regulation of gastrointestinal (GI) function is well established. However, it is only recently that the interaction of sympathetic neurons, and of sympathetic transmitters, with the GI immune system and with gut flora has begun to be explored. Changes in the behaviour of sympathetic nerves when gut function is compromised, for example in ileus and in inflammation, have been observed, but the roles of the sympathetic innervation in these and other pathologies are not adequately understood. In this article, we first review the principal roles of the sympathetic innervation of the GI tract in controlling motility, fluid exchange and gut blood flow in healthy individuals. We then discuss the evidence that there are important interactions of sympathetic transmitters with the gut immune system and enteric glia, and evidence that inflammation has substantial effects on sympathetic neurons. These reciprocal interactions contribute to pathological changes in ways that are not yet clarified. Finally, we focus on inflammation, diabetes and postoperative ileus as conditions in which there is sympathetic involvement in compromised gut function.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.318
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations185
Published2009
Admission routes2
Has abstractyes

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