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Record W4412531869 · doi:10.1080/00207144.2025.2528240

The Role of Medical Hypnosis in Alleviating Procedural Anxiety in Pediatric Interventional Radiology: A Pilot Study

2025· article· en· W4412531869 on OpenAlexaff
N. De Armas Conde, Vicky Fortin, Tatiana Cabrera

Bibliographic record

VenueInternational Journal of Clinical and Experimental Hypnosis · 2025
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsHypnosisAnxietyPsychologyMedicinePsychotherapistClinical psychologyPsychiatryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Pediatric interventional radiology procedures often use general anesthesia to manage pain and anxiety, but general anesthesia carries risks. Medical hypnosis, a noninvasive technique, has shown potential, though its application in pediatric interventional radiology is underexplored. This pilot study compared medical hypnosis and general anesthesia in pediatric interventional radiology, focusing on pre- and post-procedural anxiety and overall patient experience. The mean age of the medical hypnosis group was higher than the general anesthesia group. Post-procedural anxiety was significantly lower in the medical hypnosis group compared to the general anesthesia group (p = .003). Additionally, the overall patient experience was rated higher for medical hypnosis (p = .037). Medical hypnosis offers a viable, noninvasive approach to reducing procedural anxiety and enhancing the patient experience in pediatric interventional radiology. Larger-scale studies are needed to validate these findings and optimize medical hypnosis implementation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.418
Teacher spread0.367 · 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 designNon-randomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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