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Record W3217647025 · doi:10.1521/bumc.2021.85.4.335

Investigating executive functions in youth with OCD and hoarding symptoms

2021· article· en· W3217647025 on OpenAlexaff
Melissa Elgie, Duncan H. Cameron, Karen Rowa, Geoffrey B. Hall, Randi E. McCabe, James MacKillop, Jennifer Crosbie, Christie L. Burton, Noam Soreni

Bibliographic record

VenueBulletin of the Menninger Clinic · 2021
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenSt. Joseph’s Healthcare HamiltonUniversity of TorontoMcMaster University
Fundersnot available
KeywordsHoarding (animal behavior)Hoarding disorderPerseverationCognitive flexibilityPsychologyCognitionExecutive functionsClinical psychologyPsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Executive functions (EF) deficits are hypothesized to be a core contributor to hoarding symptoms. EF have been studied in adult hoarding populations, but studies in youth are lacking. The current study compared multiple EF subdomains between youth with obsessive-compulsive disorder (OCD) and youth with OCD and hoarding symptoms. Forty youth (8-18 years old) with a primary diagnosis of OCD were recruited. Participants were divided by hoarding severity on the Child Saving Inventory (CSI) into either the "hoarding group" (upper 33.3%) or the "low-hoarding group" (lower 66.7%). Groups were compared on EF tasks of cognitive flexibility, decision-making, and inhibitory control. Youth in the hoarding group exhibited significantly higher cognitive flexibility and lowered perseveration than the low-hoarding group. Hoarding and low-hoarding groups did not differ in any other EF subdomain. Hoarding symptoms in youth with OCD were not associated with deficits in EF subdomains; instead, youth who hoard exhibited higher cognitive flexibility compared to youth with low hoarding 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

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)0.0010.000

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 head0.021
GPT teacher head0.283
Teacher spread0.261 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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