Pain Catastrophizing Predicts Poor Response to Topical Analgesics in Patients with Neuropathic Pain
Bibliographic record
Abstract
The prevalence of neuropathic pain approaches 10% in Canada and the United States. Given the aging population and the increasing survival rates following interventions for neuropathic pain, the prevalence of neuropathic pain conditions is expected to rise significantly over the next 20 years. Although pharmacological interventions represent the dominant treatment approach for neuropathic pain, as many as 50% of patients are partially or completely refractory to the available treatments. Pain catastrophizing has been associated with heightened pain experiences in patients with neuropathic pain conditions; however, the clinical relevance of the relationship between catastrophizing and poor treatment outcomes is, to date, unclear. Accordingly, using a numerical rating scale, this study aimed to examine this relationship in patients with varied neuropathic pain conditions who completed a measure of catastrophizing before initiating a course of topical analgesic. BACKGROUND: Previous research suggests that high levels of pain catastrophizing might predict poorer response to pharmacological interventions for neuropathic pain. OBJECTIVE: The present study sought to examine the clinical relevance of the relation between catastrophizing and analgesic response in individuals with neuropathic pain. Clinically meaningful reductions were defined in terms of the magnitude of reductions in pain through the course of treatment, and in terms of the number of patients whose end‐of‐treatment pain ratings were below 4/10. METHODS: Patients (n=82) with neuropathic pain conditions completed a measure of pain catastrophizing at the beginning of a three‐week trial examining the efficacy of topical analgesics for neuropathic pain. RESULTS: Consistent with previous research, high scores on the measure of pain catastrophizing prospectively predicted poorer response to treatment. Fewer catastrophizers than noncatastrophizers showed moderate (≥2 points) or substantial reductions in pain ratings through the course of treatment. Fewer catastrophizers than noncatastrophizers achieved end‐of‐treatment pain ratings below 4/10. CONCLUSIONS: The results of the present study suggest that the development of brief interventions specifically targeting catastrophic thinking might be useful for enhancing the effects of pharmacological interventions for neuropathic pain. Furthermore, failure to account for the level of catastrophizing might contribute to null findings in clinical trials of analgesic medication.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".