Prenatal chromosomal microarray analysis: a survey of prenatal genetic counselors' experiences and attitudes
Bibliographic record
Abstract
OBJECTIVE: Studies showing the efficacy and accuracy of chromosomal microarray analysis (CMA) in prenatal diagnosis may position it as a first-tier prenatal test. This study seeks to characterize the practices and attitudes of North American prenatal genetic counselors regarding CMA. METHOD: Genetic counselors (N = 196) in Canada and the USA responded to an anonymous online survey. Completed surveys were analyzed (n = 160). RESULTS: Most respondents viewed CMA as useful (73%), presented CMA to patients (84%), and had ordered CMA at least once (69%). The use of full versus targeted arrays varied. Logistic regression analyses identified three significant predictors for the view that prenatal CMA is useful: more prenatal counseling experience, younger age, and previously presenting CMA to a patient. Three factors predicted the likelihood of offering CMA to prenatal patients: percentage of time spent in prenatal practice, belief that CMA is useful, and practicing in the USA (versus Canada). Reasons cited for not using CMA included financial concerns, the possibility of ambiguous results, and ethical concerns. Most respondents (n = 111) believed that ambiguous results are an ethical issue. CONCLUSION: Clinical guidelines for prenatal CMA, further research on specific copy number variants, and broader availability of targeted arrays to reduce ambiguous results are needed.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".