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Record W4210328554 · doi:10.1038/s41386-021-01256-3

Large-scale structural network change correlates with clinical response to rTMS in depression

2022· article· en· W4210328554 on OpenAlexafffund
Sean M. Nestor, Arsalan Mir-Moghtadaei, Fidel Vila‐Rodriguez, Peter Giacobbe, Zafiris J. Daskalakis, Daniel M. Blumberger, Jonathan Downar

Bibliographic record

VenueNeuropsychopharmacology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsCentre for Addiction and Mental HealthUniversity of British ColumbiaUniversity of TorontoUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institute of Mental HealthTemerty Family FoundationDepartment of Psychiatry, University of TorontoNational Institutes of HealthCampbell InstituteH. Lundbeck A/SUniversity of TorontoMichael Smith Health Research BCGovernment of CanadaWeston Family FoundationSunnybrook FoundationMagVentureVancouver Coastal Health Research InstituteSt. Jude MedicalCentre for Addiction and Mental HealthCanadian Institutes of Health ResearchWeston Brain InstituteCentre for Addiction and Mental Health FoundationFondation Brain CanadaBristol-Myers SquibbBrainsWayIndiviorOntario Brain InstituteKlarman Family Foundation
KeywordsTranscranial magnetic stimulationNeuroplasticityMajor depressive disorderPsychologyDorsolateral prefrontal cortexAnterior cingulate cortexNeuroscienceDepression (economics)Physical medicine and rehabilitationPrefrontal cortexMedicineStimulationCognition

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 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)0.000
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.054
GPT teacher head0.372
Teacher spread0.319 · 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

Citations22
Published2022
Admission routes2
Has abstractno

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