129: Diagnosing Fetal Alcohol Spectrum Disorder, the Need for Genetic Testing: A Pilot Study
Bibliographic record
Abstract
Fetal Alcohol Spectrum Disorder (FASD) has no specific diagnostic marker, and the facial features and growth restriction found in the full Fetal Alcohol Syndrome are found only in a small percentage of children diagnosed within the Fetal Alcohol Spectrum. The presentation of FASD often overlaps with genetic syndromes. Thus, a genetic opinion is frequently requested in the assessment of a child with suspected FASD. To study the outcome of genetic assessments of 220 children and youth referred to a FASD clinic between 2009 and 2013. Prospective study design. 220 children and youth, aged 18 months to 23 years. FASD clinic in a large teaching hospital in Ontario. Data from the medical records were abstracted, entered onto a standard study pro forma, recorded in an Excel spreadsheet, and analyzed using simple frequency analysis. Sixty-eight percent of children and youth assessed in the clinic were diagnosed with Fetal Alcohol Spectrum Disorder. Twenty-eight percent had at least one facial feature of the full Fetal Alcohol Syndrome including thin upper lip, smooth philtrum, or short palpebral fissures. Only 4% had all of the three facial features. The most common alternative diagnosis was a chromosome disorder including Fragile X, 22q11.2 deletion, Williams Syndrome and others, representing 11.2% of the FASD referrals. Genetic assessment was of particular value in excluding other diagnoses and providing information to caregivers. Structural malformations were not common in the group overall, and some previously reported diagnostic signs were not found to be reliable markers of FASD. Chromosome disorders showed phenotypic overlap with FASD, and are an important differential diagnosis. The study findings support a large multi-centre project.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".