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Record W2066765478 · doi:10.3233/978-1-60750-017-9-40

Scientific Evidence for the Effectiveness of Virtual Reality for Pain Reduction in Adults with Acute or Chronic Pain

2009· article· en· W2066765478 on OpenAlexaff
Shahnaz Shahrbanian, Xiaoli Ma, Nicol Korner‐Bitensky, Maureen J. Simmonds

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsAcute painMedicinePhysical therapyRandomized controlled trialVirtual realityChronic painIntervention (counseling)Clinical trialPain managementSystematic reviewMEDLINESurgeryAnesthesiaComputer scienceNursingInternal medicine

Abstract

fetched live from OpenAlex

The objective of this systematic review was to determine the level of scientific evidence for the effectiveness of VR for pain management in adults with pain. A comprehensive systematic search involving major health care databases was undertaken to identify randomized clinical trials (RCTs) and descriptive studies. Twenty-seven studies were identified that fulfilled the inclusion criteria. There was strong (Level 1a) evidence of a greater benefit from immersive VR and limited evidence (Level 2a) for the effectiveness of non-immersive VR in reducing acute pain. Moreover, there is limited evidence (Level 2a) of effectiveness of immersive VR compared to no VR for reducing chronic pain. There is currently no published study that has explored the effectiveness of non-immersive VR for chronic pain (level 5). It is concluded that VR can be recommended as a standard or adjunct clinical intervention for pain management at least in the management of acute pain.

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)0.032
metaresearch head score (Gemma)0.166
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.166
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.009
Bibliometrics0.0100.005
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.055
GPT teacher head0.401
Teacher spread0.347 · 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 designSystematic review
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

Citations41
Published2009
Admission routes1
Has abstractyes

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