A comparison of the prevalence of prenatal alcohol exposure obtained via maternal self-reports versus meconium testing: a systematic literature review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Maternal self-reports, used for the detection of prenatal alcohol exposure (PAE), may lack validity, necessitating the use of an objective biomarker. The detection of fatty acid ethyl esters (products of non-oxidative ethanol metabolism) in meconium has been established as a novel biomarker of PAE. The purpose of the current study was to compare the prevalence of PAE as reported via maternal self-reports with the results of meconium testing, and to quantify the disparity between these two methods. METHODS: A systematic literature search for studies reporting on the prevalence of PAE, using maternal self-reports in combination with meconium testing, was conducted using multiple electronic bibliographic databases. Pooled prevalence estimates and 95% confidence intervals (CI) were calculated based on eight studies, using the Mantel-Haenszel method, assuming a random effects model. A random effects meta-regression was performed to test for a difference. RESULTS: The pooled prevalence of PAE as measured by meconium testing was 4.26 (95% CI: 1.34-13.57) times the pooled prevalence of PAE as measured by maternal self-reports. Large variations across the studies in regard to the difference between estimates obtained from maternal self-reports and those obtained from meconium testing were observed. CONCLUSIONS: If maternal self-reports are the sole information source upon which health care professionals rely, a number of infants who were prenatally exposed to alcohol are not being recognized as such. However, further research is needed in order to validate existing biomarkers, as well as discover new biomarkers, for the detection of PAE.
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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".