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Record W2401041180 · doi:10.1097/yco.0000000000000221

Cognition in major depressive disorder

2015· review· en· W2401041180 on OpenAlexaff
Roger S. McIntyre, Yena Lee

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2015
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCognitionPsychologyMajor depressive disorderClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Cognitive dysfunction in major depressive disorder (MDD) is common, pervasive across multiple subdomains of cognitive function, and is a principle determinant of health outcomes from patient, provider, and societal perspectives. The overarching aim herein is to provide rationale for the evaluation, measurement, and specific treatment of cognitive function in adults with MDD. RECENT FINDINGS: Evidence indicates that cognitive dysfunction in MDD is a critical mediator of workplace disability. Systematic evaluation and measurement of cognitive function is warranted. All individuals with MDD should be evaluated for both objective and subjective cognitive dysfunction. Although differences between antidepressants in overall antidepressant efficacy are not consistent, unequivocal differences in improving measures of cognitive function are noted with evidence indicating that vortioxetine has multidomain cognitive benefits, whereas duloxetine has replicated evidence of improving measures of acquisition and recall (i.e. memory). SUMMARY: The probability of functional recovery in MDD is likely to increase with interventions that specifically target and improve measures of cognitive function. Clinicians are encouraged to evaluate patients using a validated measure (e.g. the THINC-it tool); prevention of cognitive impairment in MDD is a critical therapeutic priority. Vortioxetine and duloxetine benefit measures of cognitive function in MDD. Preliminary evidence of beneficial effects on cognitive emotional processing are reported with ketamine.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.095
GPT teacher head0.432
Teacher spread0.337 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations85
Published2015
Admission routes1
Has abstractyes

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