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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 OpenAlex
Shahnaz Shahrbanian, Xiaoli Ma, Nicol Korner‐Bitensky, Maureen J. Simmonds

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.015
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.055
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