Chromosome microarrays in human reproduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Chromosome microarray (CMA) testing allows automatic and easy identification of large chromosomal abnormalities detectable by conventional cytogenetics as well as the detection of submicroscopic chromosomal imbalances. METHODS: A PubMed search was performed in order to review the current use of CMA testing in the field of human reproduction. Articles discussing the use of CMA in the preimplantation setting, ongoing pregnancies, miscarriages and patients with reproductive disorders were considered. RESULTS: A high rate of concordance between conventional methods of detecting chromosomal abnormalities [e.g. fluorescence in situ hybridization (FISH), karyotyping] and CMA was reported in the prenatal setting with CMA providing more comprehensive and detailed results as it investigates the whole genome at higher resolution. In preimplantation genetic screening, CMA is replacing FISH and the selection of embryos based on CMA has already resulted in live births. For ongoing pregnancies and miscarriages, CMA eliminates tissue culture failures and artifacts and allows a quick turnaround time. The detection of submicroscopic imbalances [or copy number variants (CNVs)] is beneficial when the imbalance has a clear clinical consequence but is challenging for previously undescribed imbalances, particularly for ongoing pregnancies. Recurrent CNVs have been documented in patients with reproductive disorders; however, the application of CMA in this field is still limited. CONCLUSIONS: CMA enhances reproductive medicine as it facilitates better understanding of the genetic aspects of human development and reproduction and more informed patient management. Further clinical validation of CMA in the prenatal setting, creation of practice guidelines and catalogs of newly discovered submicroscopic imbalances with clinical outcomes are areas that will require attention in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it