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Record W4296745040 · doi:10.3389/fpsyt.2022.986378

Transcranial magnetic stimulation and intravenous ketamine combination therapy for treatment-resistant bipolar depression: A case report

2022· article· en· W4296745040 on OpenAlexaff
Laurent Elkrief, Olivier Payette, Jean-Nicolas Foucault, Christophe Longpré-Poirier, Maxime Richard, Véronique Desbeaumes Jodoin, Paul Lespérance, Jean‐Philippe Miron

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsKetamineTreatment-resistant depressionTolerabilityAntidepressantTranscranial magnetic stimulationMedicineDepression (economics)PharmacotherapyRefractory (planetary science)Deep brain stimulationElectroconvulsive therapyPsychologyAnesthesiaPsychiatryPsychotherapistStimulationPharmacologyInternal medicineSchizophrenia (object-oriented programming)Adverse effectAnxiety

Abstract

fetched live from OpenAlex

About a third of patients suffering from major depression develop treatment-resistant depression (TRD). Although repetitive transcranial magnetic stimulation (rTMS) and intravenous ketamine have proven effective for the management of TRD, many patients remain refractory to treatment. We present the case of a patient suffering from bipolar TRD. The patient was referred to us after failure to respond to first-and second-line pharmacotherapy and psychotherapy. After minimal response to both rTMS and ketamine alone, we attempted a combination rTMS and ketamine protocol, which led to complete and sustained remission. Various comparable and complimentary mechanisms of antidepressant action of ketamine and rTMS are discussed, which support further study of this combination therapy. Future research should focus on the feasibility, tolerability, and efficacy of this novel approach.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.259
Teacher spread0.249 · 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 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

Citations15
Published2022
Admission routes1
Has abstractyes

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