Bibliographic record
Abstract
Array genomic hybridization (AGH) can detect chromosomal gains or losses that are 100 times smaller than those identifiable by conventional cytogenetic methods. Genome-wide AGH can identify genomic imbalance that causes birth defects and mental retardation at least twice as frequently as conventional cytogenetic analysis. Using AGH as a prenatal test for fetal genomic imbalance offers the promise of detecting pathogenic gain or loss of genomic material more quickly and much more frequently than current methods. However, the chance of finding a result of uncertain clinical significance is much greater than with conventional cytogenetic analysis, and the benefit-cost ratio of doing AGH in addition to conventional cytogenetic analysis in pregnancies at high risk for Down syndrome is likely to be poor. Very little is known about the natural history and range of clinical variability associated with most pathogenic submicroscopic copy number variants (CNVs). It seems doubtful that patients can be adequately counseled for prenatal AGH testing in most cases because the risks and benefits are unknown. At present, AGH should be offered for prenatal diagnosis only if the pregnancy is at especially high risk of having a pathogenic CNV or if AGH is being done as part of a clinical trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".