MétaCan
Menu
Back to cohort
Record W2040624426 · doi:10.1097/nmd.0b013e31829c5030

Cognitive Remediation for Treatment-Resistant Depression

2013· article· en· W2040624426 on OpenAlexafffund
Christopher R. Bowie, Maya Gupta, Katherine Holshausen, Ruzica Jokic, Michael W. Best, Roumen Milev

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2013
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsQueen's University
FundersOntario Ministry of Research and Innovation
KeywordsNeurocognitiveCognitive remediation therapyCognitionMoodClinical psychologyPsychologyDepression (economics)Randomized controlled trialCognitive skillEffects of sleep deprivation on cognitive performanceVerbal memoryPsychiatryMedicine

Abstract

fetched live from OpenAlex

Neurocognitive impairments are observed in depression and associated with poor functioning. This study examined the efficacy and the effectiveness of cognitive remediation with supplemental Internet-based homework in treatment-resistant depression. Participants were randomized to treatment or wait list control conditions. Treatment consisted of 10 weeks of weekly group sessions and daily online cognitive exercises completed at home. The participants were assessed on cognitive, mood, motivation, and functioning measures. There was a significant time by treatment interaction for attention/processing speed and verbal memory. Changes in functioning were not significant, although improved cognition predicted improvements in functioning. Number of minutes of online exercise was associated with greater cognitive improvements. Cognitive deficits are malleable with behavioral treatment in a mood disorder characterized by severe and persistent symptoms.

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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.057
GPT teacher head0.404
Teacher spread0.347 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations140
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Nervous and Mental DiseaseSame topicMental Health Research TopicsFrench-language works237,207