Metabolomic profiling by near-infrared spectroscopy as a tool to assess embryo viability: a novel, non-invasive method for embryo selection
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The morphology of an embryo has a limited predictive value for assessing viability and ongoing pregnancy, therefore new selection tools are needed to maintain success rates with single-embryo transfer (SET). In this study, we investigated if metabolomic profiling of biomarkers of embryo culture medium by near-infrared (NIR) spectroscopy has a correlation with ongoing pregnancy in SET. METHODS: A total of 333 patients scheduled for in vitro fertilization (IVF) with SET were included in the study. Embryos were selected for transfer by morphological criteria on Days 2 and 3 of in vitro culture, and left over culture media samples were analyzed by NIR spectroscopy. RESULTS: The NIR spectral analysis produced unique metabolomic profiles that correlated to an embryo's reproductive potential. Resulting relative viability scores between positive and negative pregnancy outcomes were statistically significant (P < 0.03). A logistic regression of factors correlated to pregnancy outcomes showed that maternal age, percent fragmentation and relative viability scores all demonstrated a relationship. The extent of the correlation was determined by accuracy computation, where the accuracy of assessing viable embryos on Day 3 by metabolomic profiling was 53.6% and the accuracy of the morphological selection was 38.5%. In addition, the positive predictive value of metabolomic profiling was 0.365 and the negative predictive value was 0.830. CONCLUSIONS: NIR metabolomic profiling of spent embryo culture media was able to distinguish viable embryos from non-viable embryos for reproduction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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