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Record W7132871115

Comparing the behavioural profiles of children with Fetal Alcohol Spectrum Disorders (FASDs), Attention Deficit Hyperactivity Disorder (ADHD), and Oppositional Defiant/Conduct Disorder (ODD/CD): Working towards differential diagnosis

2008· dissertation· W7132871115 on OpenAlexfundno aff
Kelly Nash

Bibliographic record

VenueTSpace · 2008
Typedissertation
Language
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsSocioemotional selectivity theoryFetal Alcohol Spectrum DisorderFetal alcoholPrenatal alcohol exposureAttention deficit hyperactivity disorderCognitionAlcohol use disorderExploratory researchFetal alcohol syndrome
DOInot available

Abstract

fetched live from OpenAlex

Fetal Alcohol Spectrum Disorders (FASDs) is a complex disorder characterized by a pervasive pattern of cognitive and behavioural impairments. Children with FASDs have behavioural profiles that are strikingly similar to children with ADHD and ODD/CD; however no study to date has compared children with FASDs to these two groups. It was hypothesized that children with FASDs would present with a unique behavioural profile compared to typically developing children and children with ADHD and ODD/CD. Furthermore, we expected that individual items from a standardized questionnaire could be used to produce an FASD screening tool. An exploratory hypothesis investigated whether the severity of socioemotional disturbance seen in children with FASDs would be reflective of environmental risk factors. Results revealed that children with FASDs had a unique behavioural profile. Additionally, an FASD screening tool is proposed. Background factors were not found to influence the problem behaviours seen in children with FASD.

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)0.000
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.296
Teacher spread0.263 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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