The predictive role of pain catastrophising following genicular arterial embolisation for the treatment of mild and moderate knee osteoarthritis
Bibliographic record
Abstract
Abstract Knee osteoarthritis (OA) is the most common form of OA and is not currently considered to be a curable disease. Specifically, mild-to-moderate knee OA that is resistant to conservative treatment, but does not warrant joint replacement, poses a significant clinical problem. Genicular arterial embolisation (GAE) is an interventional radiological technique designed to subvert neoangiogenesis within the joint, in turn reducing pain and improving function. Preliminary data has identified a subset of patients who do not respond, despite a technically successful procedure. We therefore investigated individual differences in pain and pain perception to identify predictive pre-surgical markers for clinical outcomes. Specifically, we investigated pain catastrophising (PC) and its neural correlates using resting-state functional magnetic resonance imaging (rs-fMRI). Thirty patients participated in a presurgical assessment battery during which they completed psychometric profiling and quantitative sensory testing. A subset of seventeen patients also completed an rs-fMRI session. Patients then recorded post-surgical outcomes at 6-weeks, 3-months, 12-months and 24-months. The dorsolateral prefrontal cortex (DLPFC) served as a seed for whole-brain voxel-wise connectivity with pain catastrophising scores entered as a regressor in group analysis. Pain catastrophising was associated with a myriad of aversive psychological/lifestyle variables at baseline, as well as a predisposition for attending to pain. Surprisingly, high pain catastrophisers stood to gain the best improvements from GAE, with PC scores predicting the higher reductions in pain across all time-points. Seed-based whole-brain connectivity revealed that PCS 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 when experiencing pain. These results are an early step towards understanding outcomes from novel interventional treatments for mild-to-moderate knee OA. Data suggests that improvements in pain and function via GAE could help high catastrophisers manage their pain, and in turn, the negative associations with pain that were identified at baseline.
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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 | 0.002 |
| 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".