239 Using Magnetic Resonance Spectroscopy to Develop a Brain Biomarker of Pain in People with Hand Osteoarthritis
Bibliographic record
Abstract
Background: OA is the most common arthritis worldwide, with pain being a major symptom in this condition. During established disease, chronic pain due to OA may be aggravated by the process of central sensitisation, whereby pain processing pathways of the CNS become sensitized to peripheral nerve stimulation caused by degenerative and inflammatory disease processes. Newer brain imaging techniques involving MRI have recently enhanced research into the mechanisms of OA pain. We aimed to establish if there are distinct brain regions activated during hand OA pain that could be used as biomarkers of OA pain. Methods: We conducted a brain neuroimaging study using a Philips 3T MRI scanner with 46 participants. We investigated whether biochemical changes in the brain detectable by 1H magnetic resonance spectroscopy (MRS) were related to clinical measures of perceived pain and related symptoms, including functional activity, depression and anxiety. Brain imaging using 1H MRS was performed in brain regions including the anterior cingulate cortex and the insula cortex, which are areas involved in pain processing and implicated in central sensitisation identified from our previous work. Quantified metabolites included the inflammatory marker myo-inositol and the neurotransmitters Glx (a sum of the neurotransmitters glutamate and glutamine). Clinical scores were measured using a visual analogue scale (VAS) for pain, the Australian and Canadian Hand OA Index (AUSCAN) and the Hospital Anxiety and Depression Scale (HADS).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".