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Record W3111395398 · doi:10.5812/semj.101741

The Impact of Radiofrequency Waves on Male Infertility: A Systematic Review

2020· review· en· W3111395398 on OpenAlexaboutno aff
Leili Darvish, Azadeh Amraee, Marjan Akhavan Amjadi, Zahra Atarodi Kashani, Masoumeh Ghazanfarpour, Nasibeh Roozbeh, Fatemeh Abdi

Bibliographic record

VenueShiraz E-Medical Journal · 2020
Typereview
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsMale infertilityContext (archaeology)MedicineSpermInfertilityCINAHLScopusCochrane LibraryVitalitySemen qualityMEDLINEGynecologyAndrologyMeta-analysisInternal medicineBiologyPregnancyPsychological intervention

Abstract

fetched live from OpenAlex

Context: As the use of cellphones and other electronic devices increases, concerns about the possible effect of radiofrequency waves on health are growing. Long-term use of the cellphone may have negative effects on sperm quality. Objectives: The purpose of this research was to examine men's infertility due to the effect of radiofrequency waves. Methods: In this systematic review, language restrictions were not considered in searching the databases. Cochrane Library, Google Scholar, PubMed, EMBASE, ProQuest, CINAHL, Science Direct, MEDLINE, and Scopus were used to obtain the data from them. All data were scanned from the year 2000 until 2019. Papers selected for retrieval were evaluated by the Newcastle-Ottawa and CONSORT scales. Results: A total of 14 articles that met the inclusion criteria were ultimately assessed. Motile sperm, sperm vitality and membrane integrity, morphology, volume, total sperm count, sperm concentration, and sperm fertility were found to be influenced by radiofrequency waves. Conclusions: The results showed that RF has detrimental effects on semen parameters and due to an increase in RF wave use currently and its role in male infertility, giving information to men about adverse complications of RF is necessary. Further studies are needed to design the less harmful devices.

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)0.005
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.329
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.405
Teacher spread0.349 · 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.

Study designSystematic review
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

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

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