Pain catastrophising predicts optimal improvement in pain following genicular arterial embolisation for the treatment of mild and moderate knee osteoarthritis
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is the most common form of OA. Patients with mild-to-moderate OA, who do not respond to conservative treatment or yet warrant joint replacement, represent a significant clinical challenge. Genicular Arterial Embolisation (GAE) is a promising interventional radiological technique for OA. However, data highlight a consistent subset of patients that do not respond to GAE, despite a successful procedure. Pain Catastrophising (PC) represents a set of cognitive/affective biases to pain, linked to maladaptations in the descending pain modulatory system and has been frequently identified as a predictor of clinical outcomes. Purpose: This study aimed to investigate whether baseline pain catastrophising is associated with treatment outcomes following GAE, and to explore its neural correlates using resting-state functional magnetic resonance imaging (rs-fMRI). Research Design: A prospective, longitudinal cohort design was employed for this study. Study Sample: Thirty patients with mild-to-moderate knee OA scheduled for GAE completed a presurgical assessment including psychometric profiling and quantitative sensory testing. A neuroimaging subset of 17 patients, who met MRI safety criteria, also completed rs-fMRI. Data Collection: Participants completed outcome assessments at 6 weeks, 3 months, and 12 months post-GAE. Pain Catastrophising Scale (PCS) scores were analysed in relation to treatment outcomes and to whole-brain voxel-wise functional connectivity using the dorsolateral prefrontal cortex (DLPFC) as a seed region. PCS scores were included as regressors in rs-fMRI analyses. Results: Pain Catastrophising was associated with a myriad of psychological/lifestyle baseline variables, such as depression, anxiety and poor sleep. Surprisingly, high pain catastrophisers demonstrated the best improvements, with PC scores predicting higher reductions in pain at 6-weeks (R 2 = .18, p = .024), 3-months (R 2 = .37, p < .001) and 1-year (R 2 = .18, p = .027). Resting-state analyses revealed that catastrophising was associated with higher connectivity between the DLPFC and areas of the brain associated with pain processing, suggesting more frequent engagement of top-down modulatory processes. Conclusions: These results highlight that, interestingly, patients who catastrophise may benefit most from GAE. Potential explanations for this are discussed within. Overall, this data indicates GAE is an effective treatment for knee OA, and may be valuable at managing pain for high catastrophisers, who often fare worse in more invasive surgical procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".