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Record W1968737213 · doi:10.4018/ijcicg.2014070105

Virtual Reality as Analgesia

2014· article· en· W1968737213 on OpenAlex

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

VenueInternational Journal of Creative Interfaces and Computer Graphics · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBiofeedbackVirtual realityMeditationImmersion (mathematics)Multidisciplinary approachRehabilitationPain managementSet (abstract data type)Chronic painComputer scienceMedicineHuman–computer interactionMultimediaPhysical therapy

Abstract

fetched live from OpenAlex

This paper introduces a multidisciplinary and interactive approach to self-management of chronic pain using Virtual Reality (VR). This approach is meant to reduce the reliance on heavy use of medication and provide a non-pharmacological method for pain management. In addition, the paper discusses additional technologies that deal with issues surrounding immersion, presence, and interface design that directly impact the quality of treatment patients can obtain through VR therapy. A set of guidelines are also included that signify the importance of using biofeedback and interactive sound design to help improve rehabilitation and meditation practices for pain reduction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.306
Teacher spread0.294 · 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