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Record W4322502649 · doi:10.3389/fpain.2023.1094125

The role of virtual reality as adjunctive therapy to spinal cord stimulation in chronic pain: A feasible concept?

2023· review· en· W4322502649 on OpenAlexafffundabout
Timothy Noble, Lyndon Boone, Antonios El Helou

Bibliographic record

VenueFrontiers in Pain Research · 2023
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsHorizon Health NetworkMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsMedicineChronic painVirtual realityModalitiesNarrative reviewPhysical therapySpinal cord stimulationPhysical medicine and rehabilitationSpinal cordIntensive care medicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Spinal cord stimulation and virtual reality therapy are established and promising techniques, respectively, for managing chronic pain, each with its unique advantages and challenges. While each therapy has been the subject of significant research interest, the prospect of combining the two modalities to offer a synergistic effect in chronic pain therapy is still in its infancy. In this narrative review, we assess the state of the field combining virtual reality as an adjunctive therapy to spinal cord stimulation in chronic pain. We also review the broader field of virtual reality therapy for acute and chronic pain, considering evidence related to feasibility in the Canadian healthcare system from cost and patient satisfaction perspectives. While early results show promise, there are unexplored aspects of spinal cord stimulation combined with virtual reality therapy, particularly long-term effects on analgesia, anxiolysis, and implications on the effectiveness and longevity of spinal cord stimulation. The infrastructure for billing virtual reality as a consult service or therapy must also catch up if it is eventually used to supplement spinal cord stimulation for chronic 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 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)0.020
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.980
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.183
GPT teacher head0.473
Teacher spread0.290 · 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 teacher head, 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

Citations9
Published2023
Admission routes3
Has abstractyes

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