The importance of catastrophizing for successful pharmacological treatment of peripheral neuropathic pain
Bibliographic record
Abstract
Cory Toth, Shauna Brady, Melinda Hatfield Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada Objective: Catastrophizing may be a negative predictor of pain-related outcomes. We evaluated the impact of catastrophizing upon success of first-line pharmacotherapy in the management of neuropathic pain (NeP) due to peripheral polyneuropathy. Methods: Patients with confirmed NeP with NeP Visual Analog Scale (VAS) pain severity score ≥4 (0–10 scale) completed the Coping Strategies Questionnaire (CSQ) catastrophizing subscale at baseline. Pharmacological therapy consisting of first-line agents gabapentin, pregabalin, or a tricyclic antidepressant was initiated. Other measures examined included the Karnofsky Performance Scale, Beck Depression Inventory, EuroQol Quality of Life Health Questionnaire, and Modified Brief Pain Inventory. At 3 and 6 months, questionnaires were repeated and adverse effect reporting was completed. Outcome measures assessed were pharmacotherapy success (≥30% relief of NeP) and tolerability over 6 months of follow-up. Bivariate relationships using Pearson product-moment correlations were examined for baseline CSQ catastrophizing subscale score and the change in the NeP VAS scores and medication discontinuation. Results: Sixty-six patients were screened, 62 subjects participated, and 58 subjects (94%) completed the final follow-up visit. Greater catastrophizing was associated with poor pain relief response and greater likelihood of discontinuation of pharmacotherapy, reports of greater disability, and impaired quality of life. Duration of pain was negatively associated with likelihood of pharmacotherapy success. Conclusion: Catastrophizing exerts maladaptive effects on outcomes with pharmacotherapy in NeP patients. Detection of catastrophizing during clinical visits when pharmacological therapy is being considered can be a predictive factor for patient outcomes. Keywords: neuropathic pain, catastrophizing, coping, pharmacotherapy
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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 | 0.000 | 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) | 0.002 | 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".