A.02 Long-term outcomes in the management of central neuropathic pain syndromes
Bibliographic record
Abstract
Background: Central neuropathic pain syndromes are a result of central nervous system injury, most commonly related to stroke, spinal cord injury, or multiple sclerosis. These syndromes are much less common than peripheral etiologies, with less known regarding optimal treatment. The objective of this study was to determine the long-term clinical effectiveness of the management of central relative to peripheral neuropathic pain at tertiary pain centers. Methods: Patients diagnosed with central (n=79) and peripheral (n=710) neuropathic pain were identified from a prospective observational cohort from seven Canadian tertiary centers. Data regarding patient -characteristics, analgesic use, and patient-reported outcomes were collected at baseline and 12-month follow-up. The primary outcome was the composite of reduced average pain intensity and pain interference. Secondary outcomes included assessments of function, mood, and quality-of-life. Results: At 12-month follow-up, 13.5% (95%CI,5.6-25.8) of patients achieved ≥30% reduction in pain, whereas 38.5% (95%CI,25.3-53.0) achieved a ≥1 point reduction in pain interference; 9.6% (95%CI,3.2-21.0) of patients achieving both these measures. Patients with peripheral neuropathic pain were more likely to achieve this primary outcome at 12-months (25.3% of patients; 95%CI,21.4-29.5) (p=.012). Conclusions: Patients with central neuropathic pain were less likely to achieve a meaningful improvement in pain and function compared to patients with peripheral neuropathic pain at 12-month follow-up.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".