Role of genetic analysis of products of conception and PGT in managing early pregnancy loss
Bibliographic record
Abstract
This article considers the addition of comprehensive 24-chromosomal microarray (CMA) analysis of products of conception (POC) to a standard evaluation for recurrent pregnancy loss (RPL) to help direct treatment towards expectant management versus IVF with preimplantation genetic testing for aneuploidies (PGT-A). The review included retrospective data from 65,333 miscarriages, a prospective evaluation of 378 couples with RPL who had CMA testing of POC and the standard workup, and data from an additional 1020 couples who were evaluated for RPL but did not undergo CMA testing of POC. Aneuploidy in POC explained the pregnancy loss in 57.7% (218/378) of cases. In contrast, the full RPL evaluation recommended by the American Society for Reproductive Medicine identified a potential cause in only 42.9% (600/1398). Combining the data from the RPL evaluation and the results of genetic testing of POC provides a probable explanation for the loss in over 90% (347/378) of women. Couples with an unexplained loss after the standard evaluation with POC aneuploidy accounted for 41% of cases; PGT-A may be considered after expectant management. Conversely, PGT-A would have a limited role in those with a euploid loss and a possible explanation after the standard workup. Categorizing a pregnancy loss as an explained versus unexplained loss after the standard evaluation combined with the results of CMA testing of POC may help identify patients who would benefit from expectant management versus PGT-A.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".