Altered activity of pain processing brain regions in association with hip osteoarthritis
Bibliographic record
Abstract
Hip osteoarthritis (OA) is characterized by chronic pain, but there remains a mismatch between symptoms and radiological findings. Recently, brain connectivity has been implicated in the modulation of chronic peripheral pain, however its association with perceived pain in hip OA is not understood. We used resting-state functional magnetic resonance imaging (fMRI) to examine functional connectivity associated with pain in hip OA patients. Thirty participants with hip OA and 10 non-OA controls were recruited. Using the visual analogue scale (VAS), pain scores were obtained before and after performing a painful hip activity. All participants underwent 3.0 T resting-state fMRI, and functional connectivity of brain regions associated with pain was determined and compared between participants, and before and after hip activity. Relative to controls, functional connectivity between the secondary somatosensory cortex and left posterior insula was increased, and functional connectivity between the bilateral posterior insula and motor cortices was significantly decreased in hip OA participants. In response to painful hip activity, functional connectivity increased between the thalamus, periaqueductal grey matter and brainstem. Functional connections between brain regions associated with pain are altered in hip OA patients, and several connections are modulated by performing painful activity. Unique lateralization of left posterior insula and linked brain functional connectivity patterns allows assessment of pain perception in hip OA providing an unbiased method to evaluate pain perception and pain modulation strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".