The impact of maternal weight discrepancies on prenatal screening results for Down syndrome
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the quantitative impact of maternal weight discrepancy on the screen result for Down syndrome when using Integrated Prenatal Screening and First Trimester Combined Screening. METHODS: The study population consisted of 78,165 women undergoing prenatal screening in Ontario, Canada, and 158 pregnancies affected with Down syndrome at one Ontario center. The study assessed quantitative alterations of the multiple of the median values of first and second-trimester serum markers and the risks of Down syndrome at a set of theoretical weight discrepancies. RESULTS: Weight discrepancies have the greatest impact on screening results when the initial risk is close to the risk cut-off. When the weight discrepancy is 5 lb or greater and the denominator of the initial risk is within 50 of the risk cut-off, the chance that a screen result will change from positive to negative or from negative to positive is 47-55% for women undertaking Integrated Prenatal Screening. This chance is 33-43% for women undertaking First Trimester Combined Screening. CONCLUSION: A weight discrepancy of five or more pounds has a significant impact on the risk of Down syndrome; correction of maternal weight would improve the accuracy of the screening test.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".