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Record W4403843762 · doi:10.1038/s44184-024-00099-2

Early differences in lassitude predicts outcomes in Stanford Neuromodulation Therapy for difficult to treat depression

2024· article· en· W4403843762 on OpenAlexaff
David Benrimoh, Azeezat Azeez, Jean-Marie Batail, Xiaoqian Xiao, Derrick Matthew Buchanan, Igor D. Bandeira, Andrew Geoly, Yaakov Keynan, Ian H. Kratter, Nolan Williams

Bibliographic record

Venuenpj Mental Health Research · 2024
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill University
FundersMellam Family Foundation
KeywordsDepression (economics)NeuromodulationPsychologyRating scaleRandomized controlled trialClinical psychologyMedicineInternal medicineDevelopmental psychologyStimulation

Abstract

fetched live from OpenAlex

Stanford Neuromodulation Therapy (SNT), has recently shown rapid efficacy in difficult to treat (DTT) depression. We conducted an exploratory analysis of individual symptom improvements during treatment, correlated with fMRI, to investigate this rapid improvement in 23 DTT participants from an SNT RCT (12 active, 11 sham). Montgomery-Åsberg Depression Rating Scale item 7 (Lassitude) was the earliest to show improvements between active and sham, as early as treatment day 2. Lassitude score at treatment day 3 was predictive of response at 4 weeks post-treatment and response immediately after treatment. Participants with lower lassitude scores at treatment day 3 had different patterns of sgACC functional connectivity compared to participants with higher scores in both baseline and post-treatment minus baseline analyses. Further work will aim to first replicate these preliminary findings, and then to extend these findings and examine how SNT may affect lassitude and behavioral activation early in treatment.

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)0.002
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 score0.298
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.279
GPT teacher head0.465
Teacher spread0.186 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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