Bibliographic record
Abstract
Background: Fetal alcohol spectrum disorder (FASD) is used to describe the spectrum of birth defects due to prenatal alcohol exposure; these include craniofacial abnormalities and intellectual disabilities. The prevalence of FASD is estimated at 1 in 100. Diagnostic criteria include distinct facial features, neurodevelopmental deficits and confirmation of alcohol use during pregnancy. Unfortunately, often criteria are missed or absent. No biochemical marker is available for screening and diagnosis of FASD that is easy, accurate and cost-effective. Methods: Five children are being recruited from both the FASD clinic at the Glenrose Rehabilitation Hospital and the Healthy Infants and Children’s Clinical Research Program (HICUPP) registry. The levels of exhaled nasal NO will be measured and compared between the two groups. Metabolomics analysis on urine samples targeting metabolites of the NO pathway, along with other urinary metabolites is being performed. Bioinformatic statistical tools will be applied to determine whether measured metabolite profiles can provide distinct signatures between healthy children and children with FASD. Results: This project is ongoing. Conclusions: We hope to correlate NO levels with FASD, illustrating the relationship between NO, ciliopathies and development of FASD. As well, we hope to determine whether urinary metabolites may yield diagnostic markers of FASD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".