P64The newly calculated equations of nuchal skinfold thickness measurement in mid‐trimester
Bibliographic record
Abstract
Background Use of nuchal skinfold thickness (NT) measurement as an ultrasound marker for Down syndrome has been limited due to a high false positive rate. We suggested that some variables, which influence on NT have been presented. The purpose of our study was to identify the variables that have effects on measured values of NT, and to make a regression equation based on those variables. Method The data on gestational age (GA), cephalic index (CI), presentation (Pr; vertex or breech) and the presence or absence of nuchal cord (NC) were collected prospectively on 548 normal singleton fetuses between 16 and 24 weeks' gestation. We calculated independent correlation of those variables with NT by multiple regression analysis and made a regression equation based on GA, CI, Pr, and NC. Results GA has positive correlation and CI has negative correlation with NT significantly. The nuchal skinfold was thicker among fetuses with breech presentation rather than those of vertex presentation and increased in the presence of nuchal cord. The all four variables (GA, CI, Pr, and NC) were independent factors to NT by multiple regression analysis. We calculated the expected NT through these observations; for fetuses presenting vertex, NT = 5.608 + 0.243GA − 0.066CI + NC* and for breech, NT = 2.803 + 0.392GA − 0.066CI + NC* (*if no NC, NC* equals −0.785 and zero for the other). Conclusion This is the first report, which takes GA, CI, Pr and NC for correlation factors with NT as a whole. These equations may be considered as a screening method for the detection of aneuploidies.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".