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Record W2126018758 · doi:10.1093/arclin/acp052

Executive Functions and the Obsessive-Compulsive Disorder: On the Importance of Subclinical Symptoms and Other Concomitant Factors

2009· article· en· W2126018758 on OpenAlexaff
Marie-Josée Bédard, Christian C. Joyal, Lucie Godbout, Sophie Chantal

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2009
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsHôpital de l'Enfant-JésusUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNeuropsychologyExecutive functionsSubclinical infectionPsychologyCognitionClinical psychologyDevelopmental psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Although reviews concerning the neuropsychology of obsessive-compulsive disorder (OCD) put great emphasis on impaired executive functioning, the overall conclusions are notoriously divergent. The main goal of the present study was to use a battery of neuropsychological tasks to assess nine cognitive domains with a special focus on executive functions in 40 patients with OCD. A secondary objective was to examine the relationships between clinical or demographic variables and neuropsychological performances. The third goal was to separate executive functions in more homogeneous components to verify whether specific impairment might be found in persons with OCD. Confirming the main hypothesis, few neuropsychological differences emerged between the OCD and healthy participants when concomitant factors were controlled. Moreover, subclinical symptoms appeared to play a different and independent role on the cognitive results. Future studies should include more specific tasks of lower-order executive functions among persons with OCD to confirm this possibility.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.360
Teacher spread0.328 · 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 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

Citations64
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueArchives of Clinical NeuropsychologySame topicObsessive-Compulsive Spectrum DisordersFrench-language works237,207