Pain classification using functional near infrared spectroscopy and assessment of virtual reality effects in cancer pain management
Bibliographic record
Abstract
Objective measurements of pain and safe methods to alleviate it could revolutionize medicine. This study used functional near-infrared spectroscopy (fNIRS) and virtual reality (VR) to improve pain assessment and explore non-pharmacological pain relief in cancer patients. Using resting-state fNIRS (rs-fNIRS) data and multinomial logistic regression (MLR), we identified brain-based pain biomarkers and classified pain severity in cancer patients. Participants included healthy individuals who underwent rs-fNIRS recording without VR (Group A), cancer patients who underwent rs-fNIRS recording both before and after engaging in the Oceania relaxation program VR intervention (Group B), and cancer patients who underwent rs-fNIRS recording without VR (Group C). All participants wore a wireless fNIRS headcap for brain activity recording. Pain severity was self-reported by patients using the FACES Pain Scale-Revised (FPS-R). fNIRS data were analyzed with MLR, categorizing pain into no/mild (0-4/10), moderate (5-7/10), and severe (8-10/10) levels. The MLR model classified pain severity in an unseen test group, selected using the leave-one-participant-out technique and repeated across all participants, achieving an accuracy of 74%. VR significantly reduced pain intensity (Wilcoxon signed-rank test, P < 0.001), with significant changes in brain functional connectivity patterns (P < 0.05). Additionally, 75.61% of patients experienced pain reductions exceeding the clinically relevant threshold of 30%. These findings underscore the potential of fNIRS for pain assessment and VR as a useful non-pharmacological intervention for cancer-related pain management, with broader implications for clinical pain management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".