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Record W4390118516 · doi:10.5114/phr.2023.133717

High-induction electromagnetic field – biophysical basis and review of scientific reports

2023· article· en· W4390118516 on OpenAlexaboutno aff
Mateusz Leroch, Jakub Łaszcz, Weronika Bajer, Wojciech T. Laber, Kuba Ptaszkowski

Bibliographic record

VenuePhysiotherapy Review · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsnot available
Fundersnot available
KeywordsElectromagnetic fieldField (mathematics)MedicinePhysicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Background: In the 21st century, the first devices generating a high-induction electromagnetic field of up to 3T were used, which, in comparison to the previous century, meant a more than 70fold increase in the device's power.The novelty of the therapeutic method discussed in the article means that more needs to be scientifically proven about the effectiveness of therapy.Aims: This paper aims to present the principle of operation of the device and the possibility of its use in physiotherapy.The article considers the fundamentals of high-induction electromagnetic field applications, device construction, and its functions.Summary: Attention is paid to contraindications and recommendations set by the manufacturer, and the issue of therapeutic effects determined by the creators of the device was raised.The authors also reviewed current scientific reports and research results on a high-induction electromagnetic field and its use in various disease entities, using available research reports of international and national scope for this purpose.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.008
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.005

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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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