The Diagnosis of Fetal Alcohol Syndrome
Bibliographic record
Abstract
BACKGROUND: The estimated prevalence of fetal alcohol syndrome (FAS) is 8 for every 1000 live births. FAS has serious, lifelong consequences for the affected children and their families. A variety of professionals deal with persons who have FAS, including pediatricians, general practitioners, neurologists, gynecologists, psychiatrists, and psychotherapists. Early diagnosis is important so that the affected children can receive the support they need in a protective environment. METHODS: A multidisciplinary guideline group has issued recommendations for the diagnosis of FAS after assessment of the available scientific evidence. This information was derived from pertinent literature (2001-2011) retrieved by a systematic search in PubMed and the Cochrane Library, along with the US-American and Canadian guidelines and additional literature retrieved by a manual search. RESULTS: Of the 1383 publications retrieved by the searches, 178 were analyzed for the evidence they contained. It was concluded that the fully-developed clinical syndrome of FAS should be diagnosed on the basis of the following criteria: Patients must have at least one growth abnormality, e.g., short stature, as well as all three characteristic facial abnormalities-short palpebral fissure length, a thin upper lip, and a smooth philtrum. They must also have at least one diagnosed structural or functional abnormality of the central nervous system, e.g., microcephaly or impaired executive function. Confirmation of intrauterine exposure to alcohol is not obligatory for the diagnosis. CONCLUSION: Practical, evidence-based criteria have now been established for the diagnosis of the fully-developed FAS syndrome. More research is needed in order to enable uniform, evidence-based diagnostic assessment of all fetal alcohol spectrum disorders and optimize supportive measures for the children affected by them.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".