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Record W2047579985 · doi:10.1016/j.pain.2006.05.005

Stimulating the human midbrain to reveal the link between pain and blood pressure

2006· article· en· W2047579985 on OpenAlexaboutno aff
Alexander L. Green, Shouyan Wang, Sarah L.F. Owen, Kangning Xie, Richard G. Bittar, John Stein, David J. Paterson, Tipu Z. Aziz

Bibliographic record

VenuePain · 2006
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
FundersMedical Research CouncilJohn Templeton Foundation
KeywordsMidbrainPeriaqueductal grayBlood pressureStimulationMedicineAnesthesiaSympathetic nervous systemSympathetic activityChronic painMcGill Pain QuestionnaireCentral nervous systemInternal medicineHeart rateVisual analogue scalePhysical therapy

Abstract

fetched live from OpenAlex

The periaqueductal grey area (PAG) in the midbrain is an important area for both cardiovascular control and modulation of pain. However, the precise relationship between pain and blood pressure is unknown. We prospectively studied 16 patients undergoing deep brain stimulation of the rostral PAG for chronic pain. Pre-operatively, post-operatively, and at 1 year, pain scores were assessed using both visual analogue scores and the McGill Pain Questionnaire. Patients were tested post-operatively to determine whether electrical stimulation of the PAG would modulate blood pressure. We found that the degree of analgesia induced by deep brain stimulation of the rostral PAG in man is related to the magnitude of reduction in arterial blood pressure. We found that this relationship is linear and is related to reduced activity of the sympathetic nervous system. Thus stimulation of the PAG may partly control pain by reducing sympathetic activity as predicted by William James over a century ago.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.241
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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

Citations89
Published2006
Admission routes1
Has abstractyes

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