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Efficacy of Transcranial Magnetic Stimulation [TMS] among Major Depressive Disorder Patients: A Review

2016· review· en· W2585241098 on OpenAlexaboutno aff
J.G. Bahubali, Thulkar Sanjay, N. Mahantesh, Sudhen Sumesh Kumar

Bibliographic record

VenueAsian Journal of Nursing Education and Research · 2016
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTranscranial magnetic stimulationDeep transcranial magnetic stimulationMajor depressive disorderStimulationMedicinePhysical medicine and rehabilitationInsomniaPsychiatryPsychologyNeurosciencePhysical therapyMood

Abstract

fetched live from OpenAlex

The objective of the review is to ascertain the efficacy of transcranial magnetic stimulation therapy among major depressive patients. The various research papers reviewed to know the magnitude of depression and the efficacy of TMS. Depression is a significant contributor to the global burden of disease and affects people in all communities across the world. India has the highest rate of major depression in the world. It is estimated that by the year 2020 the burden of depression will increase to 5.7% of the total burden of disease. It clearly depicts that the need for alternative and convenient treatment modality for depression as a whole. TMS proves to be one of the effective mode of treatment for depression. In terms of its convenience of treatment it's quite promising and lesser number and severity of side effects as compared to other mode of treatment. Currently, it's under practice in countries like Canada, UK, and Australia. In India it needs to be equipped in terms of training and education to the experts, very importantly creating awareness among patients and family about the treatment facility. TMS is hope for India and it necessitates rapid paradigm shift in order to curb the title of high prevalent country with regards to depression.

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)0.002
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 score0.991
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.103
GPT teacher head0.457
Teacher spread0.355 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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