Prevalence of Fetal Alcohol Spectrum Disorder among High-Risk Children and Adolescents in a Correctional Facility.
Bibliographic record
Abstract
BACKGROUND: Fetal alcohol spectrum disorder (FASD) may be under-recognized and under-diagnosed in Israel. Fewer than 10 FASD diagnoses were reported between 1998 and 2007; however, several hundred diagnoses have been made since. Furthermore, less than 10% of surveyed Israeli pediatricians reported adequate knowledge of FASD. OBJECTIVES: To determine the prevalence of suspected FASD, to establish a database as a starting point for epidemiological studies, and to develop FASD awareness for health, social, and educational services. METHODS: A chart review was conducted at an educational facility for children and adolescents with behavioral and learning challenges. The following information was extracted: adoption status, history of alcohol/drug abuse in the biological mother, medical diagnoses, medication use, and information regarding impairment in 14 published neurobehavioral categories. Subjects were classified as: category 1 (highly likely FASD) - impairment in three or more neurobehavioral categories and evidence of maternal alcohol abuse was available; category 2 (possible FASD) - impairment in three or more neurobehavioral categories and evidence to support maternal substance abuse (type/time unspecified); and category 3 (unconfirmed likelihood of FASD) - impairment in three or more neurobehavioral categories and no information regarding the biological family. RESULTS: Of 237 files analyzed, 38 subjects (16%) had suspected FASD: 10 subjects (4%) in category 1, 5 (2%) in category 2, and 23 (10%) in category 3. Twenty-seven subjects with suspected FASD (69%) had been adopted. CONCLUSIONS: This study is the most comprehensive review of FASD among Israeli children and adolescents in a population with learning and behavior challenges.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".