Comparison of the 4-Digit Code, Canadian 2015, Australian 2016 and Hoyme 2016 fetal alcohol spectrum disorder diagnostic guidelines
Bibliographic record
Abstract
BACKGROUND: As clinicians strive to achieve consensus worldwide on how best to diagnose fetal alcohol spectrum disorders (FASD), the most recent FASD diagnostic systems show convergence and divergence. Applying these systems to a single clinical population illustrates the contrasts between them, but validation studies are ultimately required to identify the best system. METHODS: The 4-Digit-Code, Hoyme 2016, Canadian 2015 and Australian 2016 FASD diagnostic systems were applied to 1,392 patient records evaluated for FASD at the University of Washington. The diagnostic criteria and tools, the prevalence and concordance of diagnostic outcomes, and validity measures were compared between the systems. RESULTS: The proportion diagnosed with fetal alcohol syndrome (FAS) and FASD varied significantly (4-Digit-Code 2.1%, ≤79%; Hoyme 6.4%, 44%, Australian 1.8%, 29%; Canadian 1.8%, 16%). Eighty-two percent were diagnosed FASD by at least one system; only 11% by all four systems. Key factors contributing to discordance include: requiring high alcohol exposure; excluding growth deficiency; relaxing the facial criteria; requiring brain criteria that prevent diagnosis of infants/toddlers; and excluding moderate dysfunction from the spectrum. Primate research confirms moderate dysfunction (1-2 domains ≤-2 standard deviations) is the most prevalent outcome caused by PAE (FAS 5%, severe dysfunction 31%, moderate dysfunction 59%). Only the 4-Digit-Code replicated this diagnostic pattern. CONCLUSION: The needs of individuals with FASD are best met when diagnostic systems provide accurate, validated diagnoses across the lifespan, the full spectrum of outcome, the full continuum of alcohol exposure; and utilize diagnostic nomenclature that accurately reflects the association between outcome and alcohol exposure.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".