Spinal Cord Stimulation for Intractable Visceral Pain Originating from the Pelvic and Abdominal Region: A Narrative Review on a Possible New Indication for Patients with Therapy-Resistant Pain
Bibliographic record
Abstract
Aim: Visceral pain, characterized by pain that is diffuse and challenging to localize, occurs frequently and is difficult to treat. In cases where the pain becomes intractable despite optimal medical management, it can affect patients' Quality of Life (QoL). Spinal Cord Stimulation (SCS) has emerged as a potential solution for intractable visceral pain. Purpose: In this narrative review, we collected all evidence regarding the efficacy of SCS for visceral pain across various underlying conditions. Methods: A comprehensive literature search was conducted in PubMed, Embase, and Web of Science in which articles published from October 1st, 1963 up to March 7th, 2023 were identified. Results: Seventy articles were included in this review of which most were retrospective cohort studies, case series and case reports. The studies, often with a small number of participants, reported on SCS for chronic pancreatitis, anorectal pain and bowel disorders, gynaecological diagnoses, visceral pelvic pain, urological disorders and finally general visceral pain. They found positive effects on pain and/or symptom relief, opioid consumption, anxiety and depression and QoL. Complications occurred frequently but were often minor and reversible. Conclusion: Better screening and selection criteria need to be established to optimally evaluate eligible patients who might benefit from SCS. A positive outcome of a sympathetic nerve block appears to be a potential indicator of SCS effectiveness. Additionally, women receiving SCS for endometriosis had a better outcome compared to other indications. Finally, SCS could also relief functional symptoms such as voiding problems and gastroparesis. Complications could often be resolved with revision surgery. Since SCS is expensive and not always covered by standard health insurance, the incorporation of cost-analyses is recommended. In order to establish a comprehensive treatment plan, including selection criteria for SCS, rigorous prospective, possibly randomized and controlled studies that are diagnosis-oriented, with substantial follow-up and adequate sample sizes, are needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".