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Record W3042674170 · doi:10.1080/15622975.2020.1795255

Left handedness and response to repetitive transcranial magnetic stimulation in major depressive disorder

2020· review· en· W3042674170 on OpenAlexaff
Paul B. Fitzgerald, Kate E. Hoy, Zafiris J. Daskalakis

Bibliographic record

VenueThe World Journal of Biological Psychiatry · 2020
Typereview
Languageen
FieldNeuroscience
TopicHemispheric Asymmetry in Neuroscience
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Health and Medical Research Council
KeywordsTranscranial magnetic stimulationLateralityMajor depressive disorderPsychologyLeft handedStimulationAudiologyMedicinePhysical medicine and rehabilitationPsychiatryNeuroscienceCognition

Abstract

fetched live from OpenAlex

OBJECTIVES: Considerable research has demonstrated the efficacy of repetitive transcranial magnetic stimulation treatment (rTMS) in patients with major depressive disorder (MDD) with differences in effects related to laterality of stimulation. However, no systematic research has explored whether left-handed subjects respond in the same way as right-handed subjects. METHODS: = 310) were pooled and we explored whether left-handed patients with MDD responded in a similar manner to rTMS, including how they responded to both high-frequency left and low-frequency right-sided forms of treatment. RESULTS: Overall, patients with MDD who were left-handed responded to a greater degree than right-handed patients to rTMS therapy. On subgroup analysis, notably limited by small numbers in the left handed groups, this effect was seen with high-frequency left-sided treatment but not with low-frequency right-sided treatment. The overall effect of a greater response in left-handed patients was not attributable to other clinical or study variables. CONCLUSIONS: Standard forms of rTMS treatment appear to be effective in patients with MDD who are left-handed and there seems no justification for modifying the laterality of treatment application in these patients.

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)0.003
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 score0.986
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.337
Teacher spread0.294 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe World Journal of Biological PsychiatrySame topicHemispheric Asymmetry in NeuroscienceFrench-language works237,207