Comment on: Pulsed Electromagnetic Field Therapy in the Treatment of Pain and Other Symptoms in Fibromyalgia: A Randomized Controlled Study
Bibliographic record
Abstract
One possible confounding factor that may be responsible for the results obtained in this paper [Multanen et al., 2018] is that the experiment was conducted in different dwellings rather than in a controlled environment, and the radiofrequency radiation in those dwellings was not considered and hence not measured. Some people are sensitive to electromagnetic frequencies that are generated by wireless devices such as Wi-Fi routers, cordless phones, nearby cellular base stations, smart meters, etc. Indeed, one of the symptoms of electrohypersensitivity is chronic pain that includes—but is not restricted to—fibromyalgia. It is likely that the homes had different levels of radiofrequency radiation. In such environments, the potentially beneficial effects of pulsed electromagnetic field (PEMF) therapy may be outweighed or masked by the potentially harmful effects of radiofrequency radiation. I would strongly encourage the authors of this study to monitor radiofrequency radiation in the microwave band as well as intermediate frequencies on electrical wires (sometimes referred to as dirty electricity or high-frequency voltage transients) and re-examine their data with this additional information. We have also studied PEMF therapy and found significant improvement in mobility and reduction in pain of people suffering from osteoarthritis. Our exposure was conducted in the same environment, and hence variability of conditions in the environment did not influence the results [Shaw et al., 2017]. I have conducted studies with various PEMF devices and recommend they be used in an electromagnetic clean environment for optimal results. As a result, studies in environments with different levels of electrosmog exposure do not provide a valid test of the technology.
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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 | 0.035 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.036 | 0.019 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".