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Record W776080413 · doi:10.5498/wjp.v5.i2.170

Transcranial magnetic stimulation for geriatric depression: Promises and pitfalls

2015· review· en· W776080413 on OpenAlexaff
Priyadharshini Sabesan, Sudheer Lankappa, Najat Khalifa, Vasudevan Krishnan, Rahul Gandhi, Lena Palaniyappan

Bibliographic record

VenueWorld Journal of Psychiatry · 2015
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsTolerabilityTranscranial magnetic stimulationMedicineDepression (economics)Clinical trialPopulationGeriatricsRandomized controlled trialPhysical therapyPhysical medicine and rehabilitationPsychiatryInternal medicineAdverse effectStimulation

Abstract

fetched live from OpenAlex

As the global population gets older, depression in the elderly is emerging as an important health issue. A major challenge in treating geriatric depression is the lack of robust efficacy for many treatments that are of significant benefit to depressed working age adults. Repetitive transcranial magnetic stimulation (rTMS) is a novel physical treatment approach used mostly in working age adults with depression. Many TMS trials and clinics continue to exclude the elderly from treatment citing lack of evidence in this age group. In this review, we appraise the evidence regarding the safety and efficacy of rTMS in the elderly. A consistent observation supporting a high degree of tolerability and safety among the elderly patients emerged across the Randomised Controlled Trials and the uncontrolled trials. Further, there is no reliable evidence negating the utility of rTMS in the elderly with depression. We also identified several factors other than age that moderate the observed variations in the efficacy of rTMS in the elderly. These factors include but not limited to: (1) brain atrophy; (2) intensity and number of pulses (dose-response relationship); and (3) clinical profile of patients. On the basis of the current evidence, the practice of excluding elderly patients from TMS clinics and trials cannot be supported.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.073
GPT teacher head0.358
Teacher spread0.285 · 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 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

Citations86
Published2015
Admission routes1
Has abstractyes

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