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Record W2042249520 · doi:10.1089/acm.2009.0208

Extremely High-Frequency Therapy in Oncology

2010· review· en· W2042249520 on OpenAlexaff
Mikhail Teppone, Romen Avakyan

Bibliographic record

VenueThe Journal of Alternative and Complementary Medicine · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsMagna International (Canada)Canadian Transplant Association
Fundersnot available
KeywordsMedicineRadiation therapyCancerBreast cancerOncologyInternal medicineMelanomaRadiologyCancer research

Abstract

fetched live from OpenAlex

OBJECTIVE: This article represents a review of the literature, mainly from Russian sources, dealing with the therapeutic application of low-intensity electromagnetic radiation in the millimeter band applied to experimental and clinical oncology. METHOD: At the early stage of these studies, efficacy and safety of millimeter electromagnetic radiation (extremely high frequency [EHF]) was proved for various types of malignant tumors. The majority of the further studies demonstrated the high efficacy and safety of millimeter wave radiation in treating patients suffering from both benign and malignant tumors. RESULTS: Developments led to treatment on skin melanoma, cancer of the ear-nose-throat, bowel and breast cancer, cancer of the uterus, lung, and stomach, solid tumors, as well as lymphoma. The main indications for this therapy are (1) preparation prior to radical treatment; (2) prevention and treatment of side-effects and complications from chemotherapy and radiotherapy; (3) prevention of metastases, relapses, and dissemination of the tumor; (4) treatment of the paraneoplastic syndrome; and (5) palliative therapy of incurable patients. CONCLUSIONS: In spite of the fact that not all mechanisms underlying effects of EHF therapy are known as yet, this therapeutic modality has been shown to have great potential in clinical oncology from studies performed in Eastern Europe and Russia.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.063
GPT teacher head0.375
Teacher spread0.312 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations12
Published2010
Admission routes1
Has abstractyes

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