Non‐thermal extremely low frequency magnetic field effects on opioid related behaviors: Snails to humans, mechanisms to therapy
Bibliographic record
Abstract
In 1984, it was initially discovered in mice that an extremely low frequency magnetic field (ELF-MF) could attenuate opiate induced analgesia. In the past 30 years, we defined some of ELF-MF exposure and subject state conditions that can both increase and decrease nociception in snails and mice and can induce analgesia in humans. In our search for mechanisms and our desire to translate our findings to the treatment of chronic pain in humans, we pioneered the use of electroencephalography and magnetic resonance imaging to monitor effects during exposure. We have contributed to an understanding of the phenomena but a considerable amount remains to be done by us and those who have undertaken corroboratory and complimentary work. As the recipient of the 2013 d'Arsonval Award, I was invited to prepare an article for Bioelectromagnetics that highlights research findings that led to the award. Here, I have focused on our main findings associated with the effects of nociception of exposure to ELF-MF. To enrich the value of this contribution, I have put our research into the context of work of others. Further, I have suggested future directions of research and the potential for linkages and synergies associated with the extensive literature on animal orientation. Hence, it needs to be acknowledged that this is a report of our contributions and not intended as a balanced review.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".