The Impact of Enrollment in a Specialized Interdisciplinary Neuropathic Pain Clinic
Bibliographic record
Abstract
BACKGROUND: Chronic pain clinics have been created because of the increasing recognition of chronic pain as a very common, debilitating condition that requires specialized care. Neuropathic pain (NeP) is a multifaceted, specialized form of chronic pain that often requires input from multiple disciplines for assessment and management. OBJECTIVE: To determine the impact of an interdisciplinary clinic for evaluation and treatment of patients with NeP. METHODS: Patients with heterogeneous etiologies for NeP were prospectively evaluated using an interdisciplinary approach every six months. Diagnostic evaluation, comorbidity evaluation, education, and pharmacological and⁄or nonpharmacological management were completed. Severity (visual analogue scale) and features of pain (Modified Brief Pain Inventory), sleep difficulties (Medical Outcomes Study - Sleep Scale), mood⁄anxiety disruption (Hospital Anxiety and Depression Scale), quality of life (European Quality-of-Life Five-Domain index), health care resources use, patient satisfaction (Pain Treatment Satisfaction Scale and Neuropathic Pain Symptom Inventory) and self-perceived change in well-being (Patient Global Impression of Change scale) were examined at each visit. RESULTS: Pain severity only decreased after one year of follow-up, while anxiety and quality- of-life indexes improved after six months. Moderate improvements of sleep disturbance, less frequent medication use and reduced health care resource use were observed during enrollment at the NeP clinic. DISCUSSION: Despite the limitations of performing a real-world, uncontrolled study, patients with NeP benefit from enrollment in a small interdisciplinary clinic. Education and a complete diagnostic evaluation are hypothesized to lead to improvements in anxiety and, subsequently, pain severity. Questions remain regarding the long-term maintenance of these improvements and the optimal structure of specialized pain clinics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".