Models for assisted conception data with embryo‐specific covariates
Bibliographic record
Abstract
Assisted conception routinely involves multiple embryo implantation within each recipient mother, with the outcome of interest being the number and multiplicity of live births. Here we consider the situation in which covariate information, potentially predictive of outcome, is available at the embryo level for each individual implanted embryo. This presents two challenges: firstly the outcome is measured at a higher, recipient, level than the covariates of interest; and secondly it is generally not known which of the implanted embryos developed to give a successful pregnancy. In practice such data have usually been analysed by aggregation of the embryo-level covariates to the recipient-level. Here we consider and compare two alternative approaches which respect the structure of the data alongside the aggregated approach. The first is a deterministic model with separate embryo and recipient success probabilities, each determined by a set of covariates, as first proposed by Spiers and extended by Zhou and Weinberg. The second is based on a multilevel model with the correlations between embryos in the same recipient modelled by a recipient level random effect. These models are compared using two real data sets, and the model properties further explored in a simulation study.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".