Psycho-sensory relationships in chronic pain
Bibliographic record
Abstract
Background:Psychological variables contribute to pain- and injury-related outcomes. We examined the hypothesis that anatomical spread and intensity of persistent pain relate to anxiety-related variables: generalised anxiety, fear of pain and pain catastrophising.Methods:An online survey was used to gather data from 413 women with persistent pain (low back pain, n = 139; fibromyalgia syndrome, n = 95; neck pain, n = 55; whiplash, n = 41; rheumatoid arthritis, n = 37; migraine, n = 46). The spread and intensity of pain were assessed using the McGill pain chart and a Numerical Rating Scale. A Bayesian Structural Equation Model assessed if the intensity and spread of pain increased with anxiety-related variables. Men were also surveyed (n = 80), but the sample size was only sufficient for analysing if their data were consistent with the model for women.Results:Across subgroups of women, one standard deviation increase in catastrophising, generalised anxiety and fear corresponded to 27%, 7% and −1% additional pain areas and a 1.1, 0 and –0.1 change in pain intensity (on 0–10 scale), respectively. Overall, our clinical significance criterion – a 30% shift in pain variable in relation to one standard deviation increase in psychological variable – was not met. However, in subgroups it was met for pain spread (low back pain, neck pain and migraine) and pain intensity (migraine and neck pain) in relation to pain catastrophising. The model generally had low goodness-of-fit to men.Conclusion:These data support a meaningful relationship between some anxiety-related variables and pain in women for some conditions. Since the model did not consistently fit the men, we may conclude that the relationships are moderated by sex. Clinician attention to psychological variables as potential contributing factors can be justified; however, research is needed to understand the relationship and whether psychological treatment can reduce pain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".