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Record W4248161422 · doi:10.32920/ryerson.14646846.v1

Neurocognitive and Dysfunctional Belief Candidate Endophenotypes of Obsessive-Compulsive and Related Disorders

2021· preprint· en· W4248161422 on OpenAlexaff
Stephanie E. Taillefer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsToronto Metropolitan UniversityQueen's University
Fundersnot available
KeywordsEndophenotypeDysfunctional familyNeurocognitivePsychologyPerfectionism (psychology)Clinical psychologyHoarding disorderHoarding (animal behavior)EtiologyCognitionObsessive compulsivePsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Background: The current dissertation examined neurocognitive and dysfunctional belief candidate endophenotypes (CEs) across the obsessive compulsive spectrum to elucidate general versus specific factors. This study included CEs from two etiological perspectives well established in the literature. Secondary analyses examined several CEs multidimensionally and examined the relationship between CEs and of QOL. Methods: A total of 77 participants took part in this study, divided into four groups; OCD (n = 21), Hoarding Disorder (HD; n = 16), Grooming Disorders which included both Trichotillomania and Excoriation Disorder (GD; n = 18), and control participants (n = 22). Participants completed a clinical interview and battery of neurocognitive tasks and questionnaires. Results: Those with HD performed worse than controls on measures of response inhibition and set-shifting. OCD continued to predict significant variance in number sequencing. Examination of dysfunctional belief CEs revealed specificity of Responsibility/Threat beliefs and Importance/Control of Thoughts beliefs to OCD. Perfectionism/Intolerance of Uncertainty appear to be broad CEs; however, differing specificity emerged depending on the measure utilized to measure the construct. Self-report indecision revealed specificity to OCD and HD. Differing patterns of QOL impairments emerged across the spectrum. A better understanding of CEs specificity has implications for diagnostic classification, etiology, course, and treatment.

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)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.008
GPT teacher head0.261
Teacher spread0.253 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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