Pharmacological treatments of neuropathic pain: real-life comparisons using propensity score matching
Bibliographic record
Abstract
ABSTRACT: Studies comparing different drug treatments for chronic neuropathic pain (NP) are very limited. We, therefore, examined 4 recommended treatments, namely, antidepressants (duloxetine, venlafaxine, and tricyclic antidepressants), antiepileptics (gabapentine and pregabalin), weak opioids, and strong opioids, among patients with NP evaluated before first visit in a tertiary pain treatment centre and 6 months later. Patients with both a clinical diagnosis of NP and a DN4 score ≥3/7 were selected from patients enrolled in the Quebec Pain Registry. Each participant was assigned an inverse weighting of the probability of receiving any NP treatment, taking into account their age, sex, baseline pain intensity, pain duration, pain catastrophizing tendency, education level, employment, and comedications at 6-month follow-up (M6). Patients were considered as improved if they presented at least a 30% reduction on average pain intensity at M6 compared with baseline. A total of 944 patients completed both baseline and M6 evaluations. Overall, 23.0% of patients were significantly improved for pain intensity at M6. There was no significant difference in proportions patients taking or not antidepressants, gabapentinoids, or weak opioids. Among patients taking strong opioids (N = 288), 13.9% (N = 40/288) were improved vs 27.0% (177/656) of those who were not on opioids (P < 0.004). Inverse probability of treatment weighting confirmed that the proportion of patients who improved was significantly lower among those taking strong opioids compared with those who did not (P < 0.001). In conclusion, long-term use of strong opioids is a treatment suited for a limited proportion of patients with chronic NP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".