A prospective identification of neuropathic pain in specific chronic polyneuropathy syndromes and response to pharmacological therapy ☆
Bibliographic record
Abstract
Although many pharmacological agents are used in the therapy of neuropathic pain (NeP) due to polyneuropathy (PN), there are limited comparison studies comparing these agents. We evaluated patients with PN and related NeP in a tertiary care neuromuscular clinic with prospective follow-up after 3 and 6 months for degree of NeP using a Visual Analog Score (VAS). Clinical response to specific open-label pharmacotherapies was measured and compared for those patients not receiving pharmacotherapy. The severity of PN was quantified by the Toronto Clinical Scoring System (TCSS), with patients classified according to etiology of PN. Of a total of 408 patients referred for diagnosis and/or management of PN, NeP was identified in 182 patients (45%). NeP was most prevalent in patients with alcohol-associated PN. Pharmacotherapy management was provided in 91% of cases at first visit, and for 87% of cases after 6 months of follow-up. There were no serious adverse events for patients related to any medications, which included gabapentinoids, tricyclic antidepressants, anticonvulsants, cannabinoids and topical agents. Prevalence of intolerable side effects was similar amongst groups of medications. Approximated numbers needed to treat were similar between different individual oral pharmacotherapies, trending towards greater treatment efficacy with combination therapy. NeP is common in patients with PN and frequently requires pharmacotherapy management, which may be more effective with combination therapy. Future studies assessing longer duration of follow-up and quality of life changes with the use of various pharmacotherapies for management of NeP due to PN will be important.
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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 | 0.008 | 0.001 |
| 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".