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Record W4411306402 · doi:10.1016/j.brs.2025.06.008

Predictors of response and remission in treatment-resistant depression: Outcomes and trajectories of repetitive transcranial magnetic stimulation

2025· letter· en· W4411306402 on OpenAlexaff
Reinhard Janssen‐Aguilar, Mohamad Hasyizan Hassan, Huda Al-Shamali, Perry J. C. Menzies, Katharine Dunlop, Manish K. Jha, Wendy Lou, Daniel M. Blumberger, Tyler S. Kaster, Venkat Bhat

Bibliographic record

VenueBrain stimulation · 2025
Typeletter
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsCentre for Addiction and Mental HealthPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTranscranial magnetic stimulationDepression (economics)StimulationDeep transcranial magnetic stimulationTreatment-resistant depressionMedicineInternal medicinePhysical medicine and rehabilitationNeurosciencePsychologyMajor depressive disorder

Abstract

fetched live from OpenAlex

Anxiety symptoms are highly prevalent in patients with treatment-resistant depression (TRD) and have been shown to negatively influence response to repetitive transcranial magnetic stimulation (rTMS) [1]. Despite their clinical relevance, anxiety symptom dynamics remain poorly characterized in rTMS literature. Prior studies have focused on depression outcomes, with anxiety typically considered a covariate or secondary outcome [2, 3]. In this retrospective cohort study, we examined anxiety trajectories during intermittent theta burst stimulation (iTBS), a TMS treatment protocol, and identified distinct response patterns that may inform individualized treatment approaches.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.284
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2025
Admission routes1
Has abstractno

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