Prediction of Intra-Twin Birth Weight Discordance by Binary Logistic Regression Analysis
Why this work is in the frame
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Bibliographic record
Abstract
AIMS: Identification of women at high risk of intra-twin birth weight discordance is helpful in obstetric care of these pregnancies. The aim of this study is to establish an intra-twin birth weight discordance prediction model. METHODS: We created an intra-twin birth weight discordance prediction model by logistic regression, based on the 1995-1997 register twin birth data of the USA. The twin sets were randomly divided into two groups: group 1 to establish the prediction model and group 2 to validate the prediction model. Intra-twin birth weight discordance was defined as birth weight discordance > 25%. The prediction model was validated by receiver operating characteristic curve. RESULTS: A birth weight discordance prediction model including maternal age (beta = 0.069), parity (beta = 0.250), fetal gender concordance (beta = 0.041), maternal hypertension (beta = 0.368), eclampsia (beta = 0.316), other medical complication (beta = 0.165), and smoking (beta = 0.164) was established, yielded a 0.558 area under the receiver operating characteristic curve. The sensitivity, specificity, and positive predictive values were 38.1, 69.7, and 10.8%, respectively, at the cut-off value of 0.09 in group 2. CONCLUSION: A birth weight discordance prediction model that includes seven variables available during pregnancy has been established with acceptable diagnostic performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it