Visual Hypersensitivity as a Transdiagnostic Marker of Surgical Pain Response in Arthritis and Chronic Pain Syndromes
Bibliographic record
Abstract
OBJECTIVE: Nociplastic pain is pain primarily driven by the central nervous system and, unlike nociceptive pain conditions, is thought to be refractory to peripherally directed therapies. Nociplastic pain is also associated with hypersensitivity to painful and other sensory stimuli (such as visual stimuli). Nonpainful sensory measures have not been well studied in nociceptive pain conditions nor directly compared with traditional pain sensitivity measures for their discriminative value. The current study aimed to investigate visual sensitivity across multiple chronic pain conditions, particularly in the context of analgesic treatment responsivity. METHODS: We compared sensitivity with experimental visual stimulation among individuals with chronic nociceptive pain, including hip osteoarthritis, chronic pelvic pain, rheumatoid arthritis, and psoriatic arthritis. Individuals with fibromyalgia, the prototypical nociplastic condition, and pain-free controls were included for reference. Lack of analgesic response 6 months after surgery in participants with osteoarthritis and chronic pelvic pain served as a model for nociplastic pain. RESULTS: Participants across all pain conditions reported greater perceived brightness in response to visual stimulation compared with controls. Higher self-reported fibromyalgia symptom severity predicted lack of response to arthroplasty and hysterectomy. Notably, increased visual sensitivity independently predicted nonresponsiveness to surgery, whereas experimental pressure pain sensitivity did not. Visual sensitivity and fibromyalgia symptom severity together predicted greater variance in responder status than either measure alone. CONCLUSION: These findings emphasize the potential value of assessing visual sensitivity to identify pain mechanisms across different diagnostic categories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".