Time-lapse imaging algorithms rank human preimplantation embryos according to the probability of live birth
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Bibliographic record
Abstract
RESEARCH QUESTION: Can blastocysts leading to live births be ranked according to morphokinetic-based algorithms? DESIGN: Retrospective analysis of 781 single blastocyst embryo transfers, including all patient clinical factors that might be potential confounders for the primary outcome measure of live birth, was weighed using separate multi-variable logistic regression models. RESULTS: There was strong evidence of effect of embryo rank on odds of live birth. Embryos were classified A, B, C or D according to calculated variables; time to start (tSB) and duration (dB{tB - tSB}) of blastulation. Embryos of rank D were less likely to result in live birth than embryos of rank A (odds ratio [OR] 0.3046; 95% confidence interval [CI] 0.129, 0.660; P < 0.005). Embryos ranked B were less likely to result in live birth than those ranked A (OR 0.7114; 95% Cl 0.505, 1.001; P < 0.01), and embryos ranked C were less likely to result in live birth than those ranked A (OR 0.6501, 95% Cl 0.373, 1.118; P < 0.01). Overall, the LRT (Likelihood Ratio Test) p-value for embryo rank shows that there is strong evidence that embryo rank is informative as a whole in discriminating between live birth and no live birth outcomes (p = 0.0101). The incidence of live birth was 52.5% from rank A, 39.2% from rank B, 31.4% from rank C and 13.2% from rank D. CONCLUSIONS: Time-lapse imaging morphokinetic-based algorithms for blastocysts can provide objective hierarchical ranking of embryos for predicting live birth and may have greater discriminating power than conventional blastocyst morphology assessment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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