Case Report: Virtual Reality Neurofeedback Therapy as a Novel Modality for Sustained Analgesia in Centralized Pain Syndromes
Bibliographic record
Abstract
Neurofeedback (NFB) Therapy is a form of biofeedback, using the electroencephalogram (EEG) that has been in use since the 1970s, serving as a non-pharmacological intervention for epilepsy and psychiatric conditions such as anxiety, depression, insomnia, PTSD, post-concussive syndrome, and now, centralized pain. Chronic pain can increase neuronal activity and eventually causes poor modulation of pain messages. With the emergence of Virtual Reality (VR) in acute pain management, and the contraindications of opioids in chronic pain, applying novel biotechnologies seems like the next frontier in multimodal pain management. In this study, the VR and NFB technologies were fused together (VR-NFB) and used as a novel treatment modality for a 55-year-old woman who suffered from chronic pain secondary to spondylolisthesis with cervical, thoracic, and lumbar disc herniations after a motor vehicle accident with comorbid depression, anxiety, sleep deprivation, and difficulty with activities of daily living, and inability to participate in physical therapy. Our case reports on the sustained analgesia achieved for 1 year after a trial of VR-NFB, and the usefulness of neuromodulation in centralized pain syndromes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".