Potential Costs and Benefits of Incorporating PGT-A Across Age Groups: A Canadian Clinic Perspective
Bibliographic record
Abstract
OBJECTIVE: To assess the potential costs and benefits of preimplantation genetic testing for aneuploidy (PGT-A) across age groups, considering financial costs, total euploidy rates and the potential for morphology grading to predict a euploid embryo. METHODS: This study is a blinded retrospective chart review of patients who incorporated PGT-A as part of their in vitro fertilization (IVF) treatment cycle at a university-affiliated fertility clinic. Patients between 25-44 years of age undergoing IVF with intracytoplasmic sperm injection and PGT-A with autologous oocytes (n = 220) were included in this study. Number of blastocysts achieved, euploidy rates and PGT-A costs were compared between 3 age groups: <35 years, 35-37, and ≥38. Additionally, agreement on the top-quality embryo based on morphology assessment alone versus PGT-A selection was analyzed and further compared based on the number of blastocysts achieved. RESULTS: A significant negative correlation between patient age and number of embryos produced, PGT-A costs, and euploidy rates (P < 0.001) was observed. Additionally, morphology alone ratings were able to predict the top-quality euploid embryo 78% of the time in the <35 age group, but only 32% of the time in the ≥38 age group (P < 0.05), with a trend toward even lower agreement when 3 or fewer blastocysts were produced. CONCLUSION: Based on our cost analysis, it may be advantageous to incorporate PGT-A when maternal age is ≥38, given the lower financial costs associated with each cycle and the low likelihood of transferring a euploid embryo on the first attempt for this age group. Nevertheless, we acknowledge that PGT-A remains a complex decision influenced by a multitude of factors.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".