Treatment algorithm for the use of psychopharmacological agents in individuals prenatally exposed to alcohol and/or with diagnosis of fetal alcohol spectrum disorder (FASD)
Bibliographic record
Abstract
Psychotropic medication treatment of individuals who have experienced prenatal alcohol exposure (PAE) has lagged behind psychosocial interventions. Multiple psychotropic medications are often prescribed for those diagnosed with a range of neurodevelopmental disabilities and impairments of PAE (neurodevelopmental disorder associated with prenatal alcohol exposure and/or fetal alcohol spectrum disorder [ND-PAE/FASD]). Despite the diverse comorbid mental disorders, there are no specific guidelines for psychotropic medications for individuals with ND-PAE/FASD. When prescribed, concerned family members and caregivers of individuals with ND-PAE/FASD reported that polypharmacy, which was typical and adverse effects render the psychotropic medications ineffective. The objective of this work was to generate a treatment algorithm for the use of psychopharmacological agents specifically for individuals with ND-PAE/FASD. The development of decision tree for use to prescribe psychotropic medications incorporated findings from previous research and the collective clinical experience of a multidisciplinary and international panel of experts who work with individuals with ND-PAE/FASD, including an algorithm specialist. After multiple meetings and discussions, the experts reached consensus on how best to streamline prescribing along neurodevelopmental clusters. These were subdivided into four ligand-specific, receptor-acting medication targets (hyperarousal, emotional dysregulation, hyperactive/neurocognitive, and cognitive inflexibility). Each cluster is represented by a list of common symptoms. The experts recommended that prescribers first ensure adequate psychosocial and environmental, including sufficient dietary, exercise, and sleep support before prescribing psychotropic medications. Treatment then progresses through three steps of psychotropic medications for each cluster. To support established treatment goals, the most function impairing clusters are targeted first.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".