Mapping the landscape of magnetic field effects on neural regeneration and repair: a systematic review, mathematical model, and meta-analysis
Bibliographic record
Abstract
Abstract Although magnetic field exposure is a well-established diagnostic tool, its use as a therapeutic in regenerative medicine is relatively new. Our goal here was to evaluate how magnetic fields affect neural repair in vitro by performing a systematic review of the literature, mathematical modeling, and meta-analyses. The 38 included articles presented with high heterogeneity, representing 13 cell types, magnitudes ranging from 0.0002-10,000 mT, frequencies from 0-150 Hz, and exposure times lasting from one hour to several weeks. Mathematical modeling revealed that increasing magnetic field magnitude increases neural progenitor cell (NPC) viability. For regenerative processes that were not influenced by magnitude, frequency, and time, we integrated data with meta-analyses. Results revealed that magnetic field exposure increases NPC proliferation while decreasing astrocytic differentiation. Collectively, our work identifies neural repair processes that may be most responsive to magnetic field exposure and provides a framework for novel hypothesis and technology development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".