Immunoglobulin G for the Treatment of Chronic Pain: Report of an Expert Workshop
Bibliographic record
Abstract
BACKGROUND: The treatment of chronic pain is still unsatisfactory. Despite the availability of different drugs, most patients with chronic pain do not receive satisfactory pain relief or report side effects. Converging evidence implicates involvement of the immune system in the pathogenesis of different types of nociceptive and neuropathic chronic pain. DESIGN: At a workshop in Liverpool, UK (October 2012), experts presented evidence suggesting immunological involvement in chronic pain and recent data supporting the concept that the established immune-modulating drug, polyvalent immunoglobulin G (IgG), either given intravenously (IVIg) or subcutaneously (SCIg), may reduce pain in some peripheral neuropathies and a range of other pain disorders. Workshop's attendees discussed the practicalities of using IVIg and SCIg in these disorders, including indications, cost-effectiveness, and side effects. RESULTS: IgG may reduce pain in a range of nociceptive and neuropathic chronic pain conditions, including diabetes mellitus, Sjögren's syndrome, fibromyalgia, complex regional pain syndrome, post-polio syndrome, and pain secondary to pathological autoantibodies. CONCLUSIONS: IgG is a promising treatment in several chronic pain conditions. IgG is a relatively safe therapeutic strategy, with uncommon and mild side effects but high costs. Randomized, controlled trials and predictive tests are needed to better support the use of IgG for refractory chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".