산전 태아 이상 선별 검사인 Triple Marker의 유용성
Bibliographic record
Abstract
Background :Our purpose was to assess the utility of prenatal triple-marker (α- fetoprotein (AFP), β-human chorionic gonadotropin (hCG) and unconjugated estriol (uE3) testing for chromosomal abnormalities in women with Down syndrome screen-positive results Method :Total 1,082 women between 15 and 21 weeks` gestation received second trimester Down syndrome risk evaluation by triple marker testing. AFP, β-hCG and uE3 were measured by Coat-A-Counts IRMA (Diagnostic Products Corporation, LA, USA), The risk for Down syndrome was calculated using a commercially available software program (AFP Expert; Benetech Medical System, Toronto, Canada) by use of a Down syndrome risk cutoff value(1:270 at midtrimester). Karyotypes were reviewed for 32 (54.2%) of these patients who received prenatal chromosome analysis Result :Fifty nine (5.5%) patients of the 1,082 women screened were identified as positive. Two chromosome abnormalities (47,XYY and 46,XX, int (9) ) were found in the 32 patients who underwent prenatal chromosome analysis (6.3%). Any cases on the abnormal serum tests torn out not to be associated with trisomy 21 Conclusion :Although triple marker screen appears to be an effective method detecting chromosome abnormalities there is a high false positive rate. Therefore, new screening test that reduce false positive rate is need to be introduced.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".