Leveraging Virtual Reality and Augmented Reality to Combat Chronic Pain in Youth: Position Paper From the Interdisciplinary Network on Virtual and Augmented Technologies for Pain Management
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) and augmented reality (AR) interventions are emerging as promising tools in the treatment of pediatric chronic pain conditions. However, in this young field, there is little consensus to guide the process of engaging in the development and evaluation of targeted VR-based interventions. OBJECTIVE: The INOVATE-Pain (Interdisciplinary Network on Virtual and Augmented Technologies for Pain management) consortium aims to advance the field of VR for pediatric chronic pain rehabilitation by providing guidance for best practices in the design, evaluation, and dissemination of VR-based interventions targeting this population. METHODS: An interdisciplinary meeting of 16 academics, clinicians, industry partners, and philanthropy partners was held in January 2020. RESULTS: Reviewing the state of the field, the consortium identified important directions for research-driven innovation in VR and AR clinical care, highlighted key opportunities and challenges facing the field, and established a consensus on best methodological practices to adopt in future efforts to advance the research and practice of VR and AR in pediatric pain. The consortium also identified important next steps to undertake to continue to advance the work in this promising new area of digital health pain interventions. CONCLUSIONS: To realize the promise of this realm of innovation, key ingredients for success include productive partnerships among industry, academic, and clinical stakeholders; a uniform set of outcome domains and measures for standardized evaluation; and widespread access to the latest opportunities, tools, and resources. The INOVATE-Pain collaborative hopes to promote the creation, rigorous yet efficient evaluation, and dissemination of innovative VR-based interventions to reduce pain and improve quality of life for children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".