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Record W4229055973 · doi:10.1016/j.bpsc.2021.07.005

Transcranial Magnetic Stimulation Indices of Cortical Excitability Enhance the Prediction of Response to Pharmacotherapy in Late-Life Depression

2021· article· en· W4229055973 on OpenAlexafffund
Jennifer I. Lissemore, Benoit H. Mulsant, Anthony J. Bonner, Meryl A. Butters, Robert Chen, Jonathan Downar, Jordan F. Karp, Eric J. Lenze, Tarek K. Rajji, Charles F. Reynolds, Reza Zomorrodi, Zafiris J. Daskalakis, Daniel M. Blumberger

Bibliographic record

VenueBiological Psychiatry Cognitive Neuroscience and Neuroimaging · 2021
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity Health NetworkKrembil FoundationUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchAllerganGE HealthcareCenters for Medicare and Medicaid ServicesNational Institutes of HealthOntario Mental Health FoundationOntario Ministry of Health and Long-Term CarePfizerCentre for Addiction and Mental Health FoundationWeston Brain InstituteOntario Ministry of Research, Innovation and ScienceCanada Foundation for InnovationCentre for Addiction and Mental HealthH. Lundbeck A/SFamily Care FoundationCampbell InstituteAcadia UniversityDystonia Medical Research FoundationAmerican Foundation for Suicide PreventionFondation Brain CanadaCanada Research ChairsOntario Brain InstituteTakeda CanadaKlarman Family FoundationJanssen PharmaceuticalsBrainsWayBristol-Myers SquibbEli Lilly and CompanyBrightFocus FoundationNational Institute of Mental HealthPatient-Centered Outcomes Research InstituteBrain and Behavior Research Foundation
KeywordsTranscranial magnetic stimulationPharmacotherapyDepression (economics)Internal medicineMedicineVenlafaxineMajor depressive disorderPsychologyStimulationAntidepressantHippocampus

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.063
GPT teacher head0.341
Teacher spread0.278 · 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
Published2021
Admission routes2
Has abstractno

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