Spinal cord stimulation in the treatment of neuropathic pain: Current perspectives of indications, cost-effectiveness, complications and results
Bibliographic record
Abstract
Introduction: The spinal cord stimulation (SCS) has been described as a valuable neuromodulating procedure in the management of chronic and medically untreated neuropathic pain. Although, many studies have discussed the use of this technique, a question still remains regarding its efficacy in different medical conditions with different etiology in the long term. The aim of this paper is to discuss the risks, complications, cost-effectiveness and results of SCS in patients affected by chronic neuropathic pain based on the comprehensive literature review. Methods: Bibliographic search of references from 1950 to 2016 using the databases MEDLINE, LILACS, SciELO, PubMed, and applied language as selection criteria, choosing preferably recent articles written in Portuguese, Spanish or English. Results: Based on literature review, SCS is a safe, reversible, adjustable and nondestructive surgical procedure demonstrating a significant effect in the reduction of pain intensity and improvement in quality of life in these patients. Furthermore, in spite of the initial high cost to its application, SCS has been associated with lower rates of complications and high rates of cost-effectiveness when compared to standard therapies. Conclusion: Although used in medical conditions with different etiology, the procedure is still an effective and a cost-effective approach to neuropathic pain, mainly in patients affected by failed back pain syndrome (FBSS) and complex regional pain syndrome (CRPS).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".