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Record W2179151588 · doi:10.1080/00207144.2015.1099405

Hypnosis for Acute Procedural Pain:<i>A Critical Review</i>

2015· review· en· W2179151588 on OpenAlexfundno aff
Cassie Kendrick, Jim Sliwinski, Yimin Yu, Aimee K. Johnson, William Fisher, Zoltán Kekecs, Gary Elkins

Bibliographic record

VenueInternational Journal of Clinical and Experimental Hypnosis · 2015
Typereview
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsnot available
FundersNational Center for Complementary and Integrative HealthMcGill University
KeywordsHypnosisRandomized controlled trialPsychologyAdjunctIntervention (counseling)Acute painPain controlPsychotherapistClinical psychologyPhysical therapyMedicineAnesthesiaAlternative medicinePsychiatrySurgery

Abstract

fetched live from OpenAlex

Clinical evidence for the effectiveness of hypnosis in the treatment of acute procedural pain was critically evaluated based on reports from randomized controlled clinical trials (RCTs). Results from the 29 RCTs meeting inclusion criteria suggest that hypnosis decreases pain compared to standard care and attention control groups and that it is at least as effective as comparable adjunct psychological or behavioral therapies. In addition, applying hypnosis in multiple sessions prior to the day of the procedure produced the highest percentage of significant results. Hypnosis was most effective in minor surgical procedures. However, interpretations are limited by considerable risk of bias. Further studies using minimally effective control conditions and systematic control of intervention dose and timing are required to strengthen conclusions.

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)0.008
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.277
GPT teacher head0.547
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations104
Published2015
Admission routes1
Has abstractyes

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