Non-invasive brain stimulation interventions for management of chronic central neuropathic pain: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Pain can affect people regardless of age, gender or ethnicity. Chronic central neuropathic pain (CCNP) is a debilitating condition that affects populations such as stroke survivors, amputees, spinal cord injury patients and patients with multiple sclerosis, with prevalence rates between 30% and 80%. This condition can be caused by a lesion or disease affecting the somatosensory system. CCNP is notoriously drug resistant, and few effective CCNP treatment or management strategies exist. The emergence of non-invasive brain stimulation and neuromodulation techniques provide novel avenues for managing chronic central neuropathic pain. This scoping review aims to systematically identify the methods and effectiveness of non-invasive brain stimulation techniques for treating and managing chronic central neuropathic pain. METHODS AND ANALYSIS: The following databases will be searched systematically: PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Institute of Electric and Electronic Engineers (IEEE), Association of Computing Machinary (ACM) and Scopus. Additional literature will be identified by searching the reference lists of identified studies. Studies will include reviews and original research in both published and grey literatures. Two reviewers will independently screen identified studies for final inclusion. A quantitative analysis on the intervention type, application and efficacy will be synthesised along with a qualitative analysis to describe the effectiveness of each intervention. ETHICS AND DISSEMINATION: No primary data will be collected and hence formal ethics review is not required. The results of the scoping review will be presented at relevant national and international conferences, published in a peer-reviewed journal and provided to the stakeholders with plain language to be posted on their websites. This scoping review will provide a foundation to guide the development of future primary research on non-invasive brain stimulation and CCNP.
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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 | 0.056 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
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".