A literature review: The effects of magnetic field exposure on blood flow and blood vessels in the microvasculature
Bibliographic record
Abstract
The effect of magnetic field (MF) exposure on microcirculation and microvasculature is not clear or widely explored. In the limited body of data that exists, there are contradictions as to the effects of MFs on blood perfusion and pressure. Approximately half of the cited studies indicate a vasodilatory effect of MFs; the remaining half indicate that MFs could trigger either vasodilation or vasoconstriction depending on initial vessel tone. Few studies indicate that MFs cause a decrease in perfusion or no effect. There is a further lack of investigation into the cellular effects of MFs on microcirculation and microvasculature. The role of nitric oxide (NO) in mediating microcirculatory MF effects has been minimally explored and results are mixed, with four studies supporting an increase in NO activity, one supporting a biphasic effect, and five indicating no effect. MF effects on angiogenesis are also reported: seven studies supporting an increase and two a decrease. Possible reasons for these contradictions are explored. This review also considers the effects of magnetic resonance imaging (MRI) and anesthetics on microcirculation. Recommendations for future work include studies aimed at the cellular/mechanistic level, studies involving perfusion measurements both during and post-exposure, studies testing the effect of MFs on anesthetics, and investigation into the microcirculatory effects of MRI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".