Transcranial magnetic stimulation maps the neurophysiology of chronic noncancer pain: A scoping review
Bibliographic record
Abstract
BACKGROUND: Chronic noncancer pain is a global public health challenge. It is imperative to identify biological markers ("biomarkers") to understand the mechanisms underlying chronic pain and to monitor pain over time and after interventions. Transcranial magnetic stimulation (TMS) is a promising method for this purpose. OBJECTIVES: To examine differences in TMS-based outcomes between persons with chronic pain and healthy controls (HCs) and/or before versus after pain-modulating interventions and relationships between pain measures and TMS outcomes; To summarize the neurophysiological mechanisms underlying chronic pain as identified by TMS. METHODS: We searched the PubMed database for literature from January 1, 1985, to June 9, 2020, with the keywords "pain" and "transcranial magnetic stimulation." Eligible items included original studies of adult human participants with pain lasting for ≥ 6 months. We completed a narrative synthesis of the study findings stratified by chronic pain etiology (primary pain, neuropathic pain, and secondary musculoskeletal pain). RESULTS: The search yielded 1265 records. The final 12 articles included 244 patients with chronic pain (192 females, aged 35-65 years) and 169 HCs (89 females, aged 28-59 years). Abnormalities in TMS outcomes that reflect GABAergic and glutamatergic activities were associated with many of the disorders studied and were distinct for each pain etiology. Chronic primary pain is characterized by reduced intracortical inhibition and corticospinal excitability, chronic neuropathic pain shows evidence of increased excitation and disinhibition, and chronic secondary musculoskeletal pain involves low corticospinal excitability. DISCUSSION: TMS could be a useful tool for delineating the neurophysiological underpinnings of chronic pain syndromes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".