A Treatment Algorithm for Neuropathic Pain: An Update
Bibliographic record
Abstract
OBJECTIVE: The purpose of this review is to provide an update of the neuropathic pain treatment algorithm previously published by Namaka et al. in 2004. This algorithm focuses on the strategic incorporation of the latest pain therapies while providing an update of any recent developments involving medications previously listed in the algorithm. DATA SOURCES: PubMed, MEDLINE, Cochrane, and Toxnet databases were used to conduct all literature searches on neuropathic pain and targeted treatment strategies. Comprehensive search efforts in the identified databases included studies published between 1980 and 2009. The search term "neuropathic pain" was used along with each of the agents outlined in this review: pregabalin, paroxetine CR, duloxetine, tramadol XL, Tramacet, Sativex, and nabilone. STUDY SELECTION: A total of 90 studies were reviewed and selected based on level 1, 2, and 3 search strategies. DATA EXTRACTION: Level 1 search strategies were initially aimed at evidence-based trials of large sample size (N > 100), with a randomized, double-blind, placebo-controlled design conducted by investigators well versed in the specialty area of interest. A level 2 search was conducted for additional trials that had many, but not all, of the desirable traits of evidence-based trials. In addition, a level 3 search strategy was conducted to compare key findings stated in anecdotal reports of very small (N < 15), poorly designed trials with the results of well-designed, evidence-based trials identified in level 1 and/or level 2 searches. DATA SYNTHESIS: Based on a thorough evaluation of the literature, pregabalin, paroxetine CR, and duloxetine have been placed in the updated algorithm as first-line agents, while tramadol XL, Tramacet, Sativex, and nabilone function primarily as adjunctive agents. CONCLUSION: The updated algorithm provides a baseline framework from which clinicians can justify the medication they prescribe.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".