Bibliographic record
Abstract
Significant progress has been made delineating criteria for diagnosis of Fetal Alcohol Spectrum Disorders (FASD). FASD has been researched by geneticists, psychologists, neurological medical professionals, and others, for the past 35 years. However, functional central nervous system (CNS) diagnostic parameters have not yet been adequately defined to address the life-long challenges facing people with this disability. Objective This presentation proposes specific brain domains, within central nervous system parameters, to be used as a framework for FASD evaluation, diagnosis and to derive intervention recommendations for clients and their families. The proposed brain domains are clearly defined, including behavior clues to be used as a way to identify potential clients for evaluation. Methods Functional CNS parameters as described in recent literature (by CDC, IOM, U of W, and Canadian) are compared with six years of experience of the Fetal Alcohol Diagnostic Program (FADP). FADP is a community-based, family-focused diagnostic program located in Duluth, Minnesota. Results Ten specific brain domains are identified as critical to CNS diagnostic parameters for successful FASD identification and management. The ten brain domains include: achievement, adaptation, attention, cognition, executive functioning, language, memory, motor, sensory/soft neurological, and social communication. These brain domains are easily understandable by medical professionals, families, social service workers, educators etc. who are initially identifying those potential clients and/or working with them after FASD evaluation. Conclusions Incorporation of these ten brain domains into the national conversation about FASD diagnosis can de-mystify the referral, diagnosis, and follow-up procedures needed to adequately work with individuals with disabilities related to fetal alcohol spectrum disorders. Key Words: fetal alcohol spectrum disorders diagnosis, brain domains, central nervous system parameters
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.444 | 0.181 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".