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Record W4395693645 · doi:10.1515/revneuro-2024-0023

A systematic review and meta-analysis of the preclinical and clinical results of low-field magnetic stimulation in cognitive disorders

2024· review· en· W4395693645 on OpenAlexafffund
Nicholas Mellon, Brett Robbins, Rebekah van Bruggen, Yanbo Zhang

Bibliographic record

VenueReviews in the Neurosciences · 2024
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsCognitionNeuroscienceNeuromodulationDeep brain stimulationBrain stimulationCompromisePsychologyPhysical medicine and rehabilitationEffects of sleep deprivation on cognitive performanceStimulationMedicineInternal medicine

Abstract

fetched live from OpenAlex

Cognitive disorders such as major depressive disorder and bipolar disorder severely compromise brain function and neuronal activity. Treatments to restore cognitive abilities can have severe side effects due to their intense and excitatory nature, in addition to the fact that they are expensive and invasive. Low-field magnetic stimulation (LFMS) is a novel non-invasive proposed treatment for cognitive disorders. It repairs issues in the brain by altering deep cortical areas with treatments of low-intensity magnetic stimulation. This paper aims to summarize the current literature on the effects and results of LFMS in cognitive disorders. We developed a search strategy to identify relevant studies utilizing LFMS and systematically searched eight scientific databases. Our review suggests that LFMS could be a viable and effective treatment for multiple cognitive disorders, especially major depressive disorder. Additionally, longer, more frequent, and more personalized LFMS treatments tend to be more efficacious.

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)0.007
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.848
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.306
GPT teacher head0.489
Teacher spread0.184 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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