Neuropathic Pain in Elderly Patients with Chronic Low Back Painand Effects of Pregabalin: A Preliminary Study
Bibliographic record
Abstract
STUDY DESIGN: Preliminary study. PURPOSE: To assess the association of neuropathic pain with chronic low back pain (LBP) and the effect of pregabalin on neuropathic pain in the elderly. OVERVIEW OF LITERATURE: Of those with chronic LBP, 37% were predominantly presenting with neuropathic pain in young adults. Pregabalin is effective for pain in patients with diabetic neuropathy and peripheral neuralgia. No study has reported on the effects of pregabalin for chronic LBP in elderly patients yet. METHODS: Pregabalin was administered to 32 patients (age, ≥65 years) with chronic LBP for 4 weeks. Pain and activities of daily living were assessed using the Neuropathic Pain Screening Questionnaire (NePSQ), the pain DETECT questionnaire, visual analog scale, the Japanese Orthopedic Association score, the short form of the McGill Pain Questionnaire and the Roland Morris Disability Questionnaire. Modic change and spinal canal stenosis were investigated using magnetic resonance imaging. RESULTS: Altogether, 43.3% of patients had neuropathic pain according to the NePSQ and 15.6% patients had pain according to the pain DETECT. The efficacy rate of pregabalin was 73.3%. A significant effect was observed in patients with neuropathic pain after 4 weeks of administration. CONCLUSIONS: Neuropathic pain was slightly less frequently associated with chronic LBP in the elderly. Pregabalin was effective in reducing pain in patients with chronic LBP accompanied with neuropathic pain. Lumbar spinal stenosis and lower limb symptoms were observed in patients with neuropathic pain. We recommend the use of pregabalin for patients after evaluating a screening score, clinical symptoms and magnetic resonance imaging studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".