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Record W2053684808 · doi:10.1016/j.ejpain.2006.06.006

Effect of hypnotic suggestion on fibromyalgic pain: Comparison between hypnosis and relaxation

2006· article· en· W2053684808 on OpenAlexaboutno aff
Antoni Castel, Magdalena Martín Pérez, José M. Sala, A. Padrol, María Rull

Bibliographic record

VenueEuropean Journal of Pain · 2006
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsnot available
Fundersnot available
KeywordsHypnosisRelaxation (psychology)FibromyalgiaRelaxation TherapyPsychologyHypnoticVisual analogue scalePhysical therapyRelaxation techniqueAutogenic trainingGuided imageryAnesthesiaPsychotherapistMedicineAlternative medicinePsychiatryNeuroscienceAnxiety

Abstract

fetched live from OpenAlex

The main aims of this experimental study are: (1) to compare the relative effects of analgesia suggestions and relaxation suggestions on clinical pain, and (2) to compare the relative effect of relaxation suggestions when they are presented as "hypnosis" and as "relaxation training". Forty-five patients with fibromyalgia were randomly assigned to one of the following experimental conditions: (a) hypnosis with relaxation suggestions; (b) hypnosis with analgesia suggestions; (c) relaxation. Before and after the experimental session, the pain intensity was measured using a visual analogue scale (VAS) and the sensory and affective dimensions were measured with the McGill Pain Questionnaire. The results showed: (1) that hypnosis followed by analgesia suggestions has a greater effect on the intensity of pain and on the sensory dimension of pain than hypnosis followed by relaxation suggestions; (2) that the effect of hypnosis followed by relaxation suggestions is not greater than relaxation. We discuss the implications of the study on our understanding of the importance of suggestions used in hypnosis and of the differences and similarities between hypnotic relaxation and relaxation training.

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)0.039
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.006
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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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.

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

Citations79
Published2006
Admission routes1
Has abstractyes

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