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Record W1538313140 · doi:10.1139/jpn.0923

Predictors of nonresponse to cognitive behavioural therapy or venlafaxine using glucose metabolism in major depressive disorder

2009· article· en· W1538313140 on OpenAlexaffvenue
Jakub Z. Konarski, Sidney H. Kennedy, Zindel V. Segal, Mark A. Lau, Peter Bieling, Roger S. McIntyre, Helen S. Mayberg

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2009
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsBaycrest HospitalSt. Joseph’s Healthcare HamiltonUniversity of TorontoCentre for Addiction and Mental HealthUniversity Health Network
Fundersnot available
KeywordsVenlafaxineMajor depressive disorderCognitionClinical psychologyPsychologyDepression (economics)PsychiatryMedicinePsychotherapistAntidepressantAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: Longitudinal neuroimaging investigations of antidepressant treatment offer the opportunity to identify potential baseline biomarkers associated with poor outcome. METHODS: To explore the neural correlates of nonresponse to cognitive behavioural therapy (CBT) or venlafaxine (VEN), we compared pretreatment (18)F-fluoro-2-deoxy-d-glucose positron emission tomography scans of participants with major depressive disorder responding to either 16 weeks of CBT (n = 7) or VEN treatment (n = 9) with treatment nonresponders (n = 8). RESULTS: Nonresponders to CBT or VEN, in contrast to responders, exhibited pretreatment hypermetabolism at the interface of the pregenual and subgenual cingulate cortices. LIMITATIONS: Limitations of our study include the small sample sizes and the absence of both arterial sampling to determine absolute glucose metabolism and high-resolution structural magnetic resonance imaging coregistration for region-of-interest analyses. CONCLUSION: Our current findings are consistent with those reported in previous studies of relative hyperactivity in the ventral anterior cingulate cortex in treatment-resistant populations.

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 score0.308
Threshold uncertainty score0.373

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 head0.033
GPT teacher head0.330
Teacher spread0.297 · 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

Citations147
Published2009
Admission routes2
Has abstractyes

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