Quantitation of phosphatidylethanols in dried blood spots to determine rates of prenatal alcohol exposure in Ontario
Bibliographic record
Abstract
BACKGROUND: Estimating rates of prenatal alcohol exposure (PAE) in a population is necessary to ensure that proper medical and social supports and interventions are in place. This study sought to estimate PAE in Ontario, Canada by quantifying phosphatidylethanol (PEth) homologues in over 2000 residual neonatal dried blood spots (DBS). METHODS: A random selection of 2011 residual DBS collected over a 1-week time period were anonymized and extracted. A targeted liquid chromatography-mass spectrometry method was used to quantify 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanol (PEth (16:0/18:1) or POPEth), the clinically accepted biomarker, and six additional PEth homologues. A POPEth level above the United States Drug Testing Laboratories (USDTL) cutoff up to 4 weeks predelivery was indicative of PAE. All PEth homologues were correlated to one another and logistic regression was used to determine the association between PAE status and infant characteristics. RESULTS: The estimated rate of PAE in Ontario, up to the last 4 weeks of gestation, was 15.5% (POPEth >28.5 nM). Most PEth homologues were moderately to strongly correlated to one another. A low birth weight and preterm birth were both associated with PAE, while being small for gestational age had lower odds of PAE. CONCLUSIONS: The results of this study suggest that PAE may be more prevalent in Ontario than previous estimates by self-report or meconium testing. These findings support the need to consider the effectiveness of current interventions and the design of new interventions to address this significant public health issue.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".