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Record W6962638057 · doi:10.17605/osf.io/cp6kh

Reward sensitivity and cognitive biases as predictors of response to low-frequency rTMS applied to the right dorsolateral prefrontal cortex in depression

2020· other· en· W6962638057 on OpenAlexaff

Bibliographic record

VenueOpen Science Framework · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTranscranial magnetic stimulationDorsolateral prefrontal cortexDepression (economics)CognitionPrefrontal cortexMajor depressive disorderCognitive bias

Abstract

fetched live from OpenAlex

Repetitive transcranial magnetic stimulation (rTMS) has been shown to be an effective treatment for individuals diagnosed with Major Depressive Disorder (MDD). However, responses to rTMS vary across individuals, and not everyone who receives rTMS treatment will experience full remission of their depression symptoms. Furthermore, changes in psychological processes that may underlie improvements in depressive symptoms over the course of low-frequency rTMS for MDD applied to the right dorsolateral prefrontal cortex are not well characterized. For the current study, we will examine whether performance on experimental measures of reward sensitivity and cognitive biases prior to commencing rTMS treatment predict symptom remission among individuals diagnosed with MDD. We will also assess changes in reward sensitivity and cognitive biases from pre-treatment to 12-week follow-up and examine whether symptom improvements are associated with changes in reward sensitivity and cognitive biases.

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)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.305
Teacher spread0.291 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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