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Record W2025272371 · doi:10.1097/nmd.0b013e3181b0be76

Cognitive Bias to Symptom and Obsessive Belief Threat Cues in Obsessive-Compulsive Disorder

2009· article· en· W2025272371 on OpenAlexaff
Judith M. Laposa, Neil A. Rector

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2009
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsObsessive compulsivePsychologyAnxietyCognitionCognitive biasPriming (agriculture)Clinical psychologyAnxiety disorderDepression (economics)Psychiatry

Abstract

fetched live from OpenAlex

The current study examined the extent to which patients with obsessive compulsive disorder (OCD) demonstrate cognitive biases to OCD symptom or inflated responsibility threat cues. Participants with either primary contamination-washing or doubting/harming-checking OCD, non-OCD anxiety disorders, and student controls completed a primed lexical decision task. Following either neutral or OCD-threat priming conditions, participants made lexical decisions regarding different sets of word stimuli: nonwords, OCD symptoms, OCD inflated responsibility, and depression. Following the OCD primes, the primary contamination-washing symptom subgroup showed increased interference on OCD symptom words compared with the harming symptom and student groups. The primary contamination-washing subgroup also showed increased interference on responsibility words compared with the harming, non-OCD anxious and student groups. However, subsidiary analyses comparing patients with contamination obsessions with and without associated fears of harming others through the spreading of contaminants, demonstrated that it was the latter group that evidenced cognitive biases to responsibility threat cues. These results are considered in relation to cognitive models of OCD.

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 score0.298
Threshold uncertainty score0.957

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 head0.017
GPT teacher head0.308
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.

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

Citations9
Published2009
Admission routes1
Has abstractyes

Explore more

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