Bibliographic record
Abstract
Preimplantation genetic testing for aneuploidy was developed as an invasive embryo-selection technique and is extensively used in in vitro fertilization (IVF) cycles. Around 95,000 preimplantation genetic testing cycles were carried out in the United States between 2014 and 2016, the majority of which were performed for aneuploidy. The objective of preimplantation genetic testing for aneuploidy is to select for transfer a euploid embryo, after embryo biopsy and cytogenetic analysis. The current technique consists of applying comprehensive chromosome screening on trophectoderm cells after blastocyst-stage embryo biopsy. This article reviews all the published randomized controlled trials on preimplantation genetic testing for aneuploidy with comprehensive chromosome screening and comments on the subject of embryo mosaicism detected by this technique. Most of these trials have been criticized because they only included good prognosis patients having normal ovarian reserve producing a high number of embryos available for biopsy. Preimplantation genetic testing for aneuploidy does not improve ongoing pregnancy rates per cycle started when routinely applied on the general IVF population but seems to be a good tool of embryo selection for a selected category of patients with normal ovarian reserve, yet should be only practiced by experienced IVF clinics. If no euploid embryo is available after preimplantation genetic testing for aneuploidy, a low-level mosaic embryo can be considered and prioritized for transfer after appropriate genetic counseling.
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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 | 0.004 | 0.003 |
| 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) | 0.004 | 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".