Evaluation of the neurobehavioral screening tool in children with fetal alcohol spectrum disorders (FASD).
Bibliographic record
Abstract
BACKGROUND: There is a growing need for validated tools to screen children at risk of fetal alcohol spectrum disorders (FASD). The Neurobehavioral Screening Tool (NST) is one of several promising screening measures for FASD, though further evidence is needed to establish the tool's psychometric utility. OBJECTIVE: To assess the predictive accuracy of the NST among children with an FASD diagnosis, with prenatal alcohol exposure (PAE) but no FASD diagnosis, and typically developing controls. METHOD: The NST was completed by caregivers of children ages 6 to 17, including 48 with FASD, 22 with PAE, and 32 typically developing non-exposed controls. Predictive accuracy coefficients were calculated using Nash et al. (2006) criteria, and compared against controls. An alternative scoring scheme was also investigated to determine optimum referral thresholds using item-level total scores. RESULTS: The NST yielded 62.5% sensitivity for participants with FASD and 50% for PAE. Specificity values were 100% with no typically developing control scoring positive. Within the FASD group there was a trend for higher sensitivity among adolescents aged 12 to17 (70.8%) compared with children aged 6 to 11 years (54.2%), p = 0.23. CONCLUSION: The findings support a growing body of literature evidencing psychometric promise for the clinical utility of the NST as an FASD screening tool, though further research on possible age-effects is warranted. The availability of a validated clinical screening tool for FASD, such as the NST, would aid in accurately screening a large number of children and lead to a timelier diagnostic referral.
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 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".