Static magnetic field exposure in 1.5 and 3 Tesla <scp>MR</scp> scanners does not influence pain and touch perception in healthy volunteers
Bibliographic record
Abstract
BACKGROUND: Magnetic field therapy is a popular approach to pain therapy, but scientific evidence on treatment effects or even effects on sensory and pain perception in healthy controls is scarce. METHODS: In the present randomized, placebo-controlled study, we investigated the influence of static magnetic field exposure on sensory (touch) and pain (pinprick, pressure and heat) perception. Eighteen healthy volunteers (age: 23 ± 2 years, nine women) underwent three 10-min static magnetic field exposures using field strengths of 0 T (placebo), 1.5 T and 3 T within clinical MR scanners in randomized order on three separate days. Participants were blinded to magnetic field strength. Experimental sensory and pain testing was performed immediately before and after each magnetic field exposure. RESULTS: There was no significant effect of field strength on the assessed experimental sensory and pain testing parameters (mechanical detection threshold, pinprick threshold, pressure pain threshold, heat pain threshold and suprathreshold heat pain rating). CONCLUSION: We found no evidence that a 10-min 1.5 T or 3 T static magnetic field exposure affects experimental sensory or pain perception in young healthy volunteers. SIGNIFICANCE: We used clinical MR scanners to investigate the effect of magnetic fields on pain perception. Using a rigorous, straightforward, placebo-controlled design, no effect of static magnetic fields on human experimental pain perception was detected. This provides a base for a more systematic investigation of magnetic field effects on pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".