Comparative Genomic Hybridization
Bibliographic record
Abstract
OBJECTIVE: To demonstrate the effectiveness of comparative genomic hybridization (CGH) for analysis of reproductive pathology specimens in clinical cytogenetics laboratories. DESIGN: A total of 856 CGH analyses were performed on various placental and fetal tissues derived from 368 specimens of spontaneous abortions and on placentas from 219 pregnancies with live-born infants. The live-born infants were clinically evaluated as normally developed, with either a normal birth weight or with intrauterine growth restriction; some live-born infants had an abnormal prenatal triple screen with normal cytogenetic results on amniotic fluid cell cultures. RESULTS: Comparative genomic hybridization analysis was successfully performed on 856 samples from spontaneously aborted specimens and term placentas. Failure of analysis occurred in 1.6% of samples and was due to an insufficient amount of tissue for DNA extraction. Comparative genomic hybridization identified aneuploidy in 53% of spontaneous abortion samples and 3.1% of term placentas. CONCLUSIONS: Comparative genomic hybridization analysis is a useful clinical tool for detection of aneuploidy in placental and fetal tissues. It provides a genome-wide screen while eliminating tissue culture failures, culture artifacts, and maternal cell contamination. We present practical guidelines for interpreting CGH profiles derived from human reproductive specimens.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".