Personalization of IVF-ICSI workflow based on patient characteristics improves IVF laboratory outcomes and embryo ploidy by PGT-A
Bibliographic record
Abstract
BACKGROUND: Intracytoplasmic sperm injection (ICSI) has become a common method of fertilization in assisted reproduction worldwide. However, there are still gaps in knowledge of the ideal IVF-ICSI workflow including the optimal duration of time between induction of final oocyte maturation, oocyte denudation and ICSI. The aim of this study was to examine outcomes following different workflow protocols in IVF-ICSI procedures in blastocysts that have undergone undisturbed incubation and preimplantation genetic testing for aneuploidy (PGT-A) prior to transfer. METHODS: Retrospective secondary analysis of 113 patients (179 IVF cycles, 713 embryos), all of whom have gone through IVF-ICSI and PGT-A using undisturbed culture. Predictive test variables were the length of time from: trigger to OPU, OPU to denudation, and denudation to ICSI. Outcome metrics assessed were: maturation, fertilization, blastulation and euploid rates. Generalized Estimated Equations Linear Model was used to examine the relationship between key elements of a given cycle and continuous outcomes and LOESS curves were used to determine the effect over time. RESULTS: In a paired multi-regression analysis, where each patient served as its own control, delaying OPU in patients with unexplained infertility improved both maturation and blastulation rates (b = 29.7, p < 0.0001 and b = 9.1, p = 0.06, respectively). Longer incubation with cumulus cells (CCs) significantly correlated with improved ploidy rates among patients under 37, as well as among patients with unexplained infertility (r = 0.22 and 0.29, respectively), which was also evident in a multiple regression analysis (b = 6.73, p < 0.05), and in a paired analysis (b = 6.0, p < 0.05). Conversely, among patients with a leading infertility diagnosis of male factor, longer incubation of the denuded oocyte prior to ICSI resulted in a significantly higher euploid rate (b = 15.658, p < 0.0001). CONCLUSIONS: In this study we have demonstrated that different IVF-ICSI workflows affect patients differently, depending on their primary infertility diagnosis. Thus, ideally, the IVF-ICSI workflow should be tailored to the individual patient based on the primary infertility diagnosis. This study contributes to our understanding surrounding the impact of IVF laboratory procedures and highlights the importance of not only tracking "classic" IVF outcomes (maturation, fertilization, blastulation rates), but highlights the importance that these procedures have on the ploidy of the embryo.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".