Prediction of pregnancy: a joint model for longitudinal and binary data
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of predicting the achievement of successful pregnancy, in a population of women undergoing treatment for infertility, based on longitudinal measurements of adhesiveness of certain blood lymphocytes. A goal of the analysis is to provide, for each woman, an estimated probability of becoming pregnant. We discuss various existing approaches, including multiple t-tests, mixed models, discriminant analysis and two-stage models. We use a joint model developed by Wange et al. (2000), consisting of a linear mixed effects model for the longitudinal data and a generalized linear model (glm) for the primary endpoint, (here a binary indicator of successful pregnancy). The joint longitudinal/glm model is analogous to the popular joint models for longitudinal and survival data. We estimate the parameters using Bayesian methodology.
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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 | 0.000 |
| 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) | 0.000 | 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