The impact of an ADHD co-morbidity on the diagnosis of FASD.
Bibliographic record
Abstract
OBJECTIVE: Many children with Fetal Alcohol Spectrum Disorders (FASD) also have co-morbid ADHD. The goal of this study was to examine the impact of having a co-morbid ADHD diagnosis on FASD diagnostic results. We compared children with FASD to those with FASD and co-morbid ADHD across the neurobehavioral domains recommended by the Canadian Guidelines in the diagnosis of FASD. METHODS: We retrospectively analyzed data from 52 children, aged 4 to 17 years, diagnosed with an FASD at a hospital FASD clinic. Thirty-three of these children had a co-morbid diagnosis of ADHD and 19 did not. Children with FASD and those with FASD and co-morbid ADHD were compared on the following neurobehavioral domains: sensory/motor, cognition, communication, academic achievement, memory, executive functioning, attention, and adaptive behavior. RESULTS: Children with FASD and ADHD performed significantly worse than those without ADHD on attention but better on academic achievement. No other group differences were significant. CONCLUSIONS: Having an ADHD co-morbidity had little effect on the FASD diagnosis. The results of this project will inform the diagnostic process for FASD and have implications for standardizing diagnostic processes across clinics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".