Alternative Medicine Techniques Have Non-Linear Effects on Radiation Response and Can Alter the Expression of Radiation Induced Bystander Effects
Bibliographic record
Abstract
Many so-called "alternative medicine" techniques such as Reiki and acupuncture produce very good outcomes for intractable pain and other chronic illnesses but the efficacy is often dismissed as being psychosomatic. However a plausible mechanism does exist i.e. that the treatments alter the electromagnetic fields in living organisms and thereby prevent or reduce activity of neurons which lead to the pain. Low doses of ionising radiation have similar effects on electromagnetic fields and are known to induce signaling cascades in tissues due to ion gradients. To test this hypothesis cell cultures were exposed to Reiki - like and to acupuncture - like treatments, both performed by qualified practitioners. The cells were exposed either before or after the treatment to x-rays and were monitored for production of direct damage or bystander signals. The data suggest that the alternative techniques altered the response of cells to direct irradiation and altered bystander signal mechanisms. We conclude that alternative medicine techniques involving electromagnetic perturbations may modify the response of cells to ionizing radiation. In addition to the obvious implications for mechanistic studies of low dose effects, this could provide a novel target to exploit in radiation protection and in optimizing therapeutic gain during radiotherapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".