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Record W4381140811 · doi:10.1192/bjp.2023.81

Effect of prior pharmacotherapy on remission with sequential bilateral theta-burst versus standard bilateral repetitive transcranial magnetic stimulation in treatment-resistant late-life depression

2023· article· en· W4381140811 on OpenAlexafffund
Rafae A. Wathra, Benoit H. Mulsant, Zafiris J. Daskalakis, Jonathan Downar, Shawn M. McClintock, Sean M. Nestor, Tarek K. Rajji, Alisson Paulino Trevizol, Daniel M. Blumberger

Bibliographic record

VenueThe British Journal of Psychiatry · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsToronto Dementia Research AllianceUniversity of TorontoSunnybrook Health Science CentreCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchTemerty Family FoundationCampbell Family Mental Health Research InstituteCentre for Addiction and Mental Health
KeywordsTranscranial magnetic stimulationDepression (economics)StimulationPharmacotherapyClinical trialMedicineRandomized controlled trialPsychologyInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Summary Repetitive transcranial magnetic stimulation (rTMS) is used for treatment of late-life depression. In the FOUR-D study, sequential bilateral theta-burst stimulation (TBS) had comparable remission rates to standard bilateral rTMS. Data were analysed from the FOUR-D trial to compare remission rates between two types of rTMS based on the number and class of prior medication trials. The remission rate was higher in participants with ≤1 previous trial (43.9%) than in participants with 2 previous trials (26.5%) or ≥3 previous trials (24.6%; χ² = 6.36, d.f. = 2, P = 0.04). Utilising rTMS earlier in late-life depression may lead to better outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations13
Published2023
Admission routes2
Has abstractyes

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