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Record W2137520321 · doi:10.1002/bem.20258

The influence of extremely low frequency magnetic fields on cytoprotection and repair

2006· review· en· W2137520321 on OpenAlexafffund
John A. Robertson, Alex W. Thomas, Y Bureau, Frank S. Prato

Bibliographic record

VenueBioelectromagnetics · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health Research
KeywordsBioelectromagneticsIschemiaNitric oxideCytoprotectionMedicineShock (circulatory)NeuroscienceMagnetic fieldCardiologyBiologyInternal medicineOxidative stressPhysics

Abstract

fetched live from OpenAlex

Ischemia-reperfusion injuries, such as those suffered from various types of cardiovascular disease, are major causes of death and disability. For relatively short periods of ischemia, much of the damage is potentially reversible and in fact, does not occur until the influx of oxygen during the reperfusion stage. Because of this, there is a window of opportunity to protect the ischemic tissue. Here, we review several mechanisms of protection, such as heat shock proteins, opioids, collateral blood flow, and nitric oxide induction, and the evidence indicating that magnetic fields may be used as a means of providing protection via each of these mechanisms. While there are few studies demonstrating direct protection with magnetic field therapies, there are a number of published reports indicating that electromagnetic fields may be able to influence some of the biochemical systems with protective applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.010
GPT teacher head0.253
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations43
Published2006
Admission routes2
Has abstractyes

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