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Record W1974909820 · doi:10.1076/chin.6.2.129.7057

Subtypes of Psychopathology in Children Referred for Neuropsychological Assessment

2000· article· en· W1974909820 on OpenAlexaff
Cory D. Saunders, Elizabeth J. Hall, Joseph E. Casey, John D. Strang

Bibliographic record

VenueChild Neuropsychology · 2000
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsWindsor Regional Hospital
Fundersnot available
KeywordsPsychopathologyPsychologyNeuropsychologyCognitionTypologyCluster (spacecraft)Clinical psychologyNeuropsychological assessmentCognitive deficitAttention deficit hyperactivity disorderDevelopmental psychologyPsychiatryCognitive impairment

Abstract

fetched live from OpenAlex

The validity of a Personality Inventory for Children-Revised edition (PIC-R) typology was examined in a sample of 323 children aged 6-16 years. These children had been referred to a children's mental health centre for neuropsychological assessment. In study 1, K-means cluster analysis (k = 12) was applied to the PIC clinical scales in an attempt to replicate the 12 clusters identified by Gdowski, Lachar, and Kline (1985). Partial cluster replication was achieved. Examination of the obtained clusters revealed significant overlap, suggesting that fewer clusters would represent an optimal solution. In study 2, a two-stage cluster analysis yielded a seven-cluster solution consistent with several key forms of psychopathology previously reported in the literature using specific neuropsychological populations. Identified subtypes included profiles characterized as: normal, cognitive deficit, cognitive deficit with internalized psychopathology, cognitive deficit with social impairment, cognitive deficit with hyperactivity, cognitive deficit with both internalized and externalized psychopathology, and combined internalized and externalized psychopathology without a cognitive deficit component.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.349
Teacher spread0.320 · 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

Citations10
Published2000
Admission routes1
Has abstractyes

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