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Different Autonomic Responses to Experimental Pain in IBS Patients and Healthy Controls

2006· article· en· W2037470962 on OpenAlexaff
Yannick Tousignant‐Laflamme, Philippe Goffaux, Patricia Bourgault, Serge Marchand

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2006
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineHypoalgesiaAutonomic nervous systemHeart rateIrritable bowel syndromeAnesthesiaHeart rate variabilityNociceptionHyperalgesiaStimulus (psychology)Threshold of painCold pressor testInternal medicineCardiologyBlood pressurePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Pain perception ratings in irritable bowel syndrome (IBS) patients suggest that they present either hypoalgesia or hyperalgesia. However, little is known about the physiologic responses these patients present to a somatic painful stimulus. GOALS: The main goal of this project was to study autonomic nervous system responses and the cardiac response to experimental pain in IBS patients. STUDY: This was addressed by exposing 27 women, 14 IBS, and 13 healthy controls (HCs), to a cold water (7 degrees C) immersion test of the forefoot for 2 minutes. Pain perception, galvanic skin responses (GSR), and heart rate (HR) were monitored during and after the immersion. RESULTS: For comparable pain perception, a significant group difference (P<0.02) in the cardiac response was observed during the immersion where the peak rise in HR was much higher for HCs, reaching 22%, whereas it only reached 8% for IBS patients. Moreover, HR variability analysis demonstrated that IBS and HCs had opposite autonomic cardiac reactivity to pain, where IBS had increased parasympathetic/decreased sympathetic reactivity. CONCLUSIONS: For comparable pain perception, IBS subject demonstrated different autonomic nervous system response to pain, which supports the view of autonomic dysregulation in IBS.

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 score0.034
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.360
Teacher spread0.329 · 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

Citations54
Published2006
Admission routes1
Has abstractyes

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