Predictors of Prenatal Screening for Fragile X Syndrome
Bibliographic record
Abstract
Background: We sought to determine the uptake rate and predictors of acceptance of fragile X DNA molecular analysis among pregnant women who are offered this testing. Methods: We conducted a retrospective cohort study of pregnant patients who met with a genetic counselor in our Prenatal Diagnosis Center. The primary outcome was undergoing fragile X carrier screening. Hypothesized predictors included gestational age, insurance status, family history, the genetic counselor with whom the patient met, duration of the counseling session, and whether the patient underwent amniocentesis or chorionic villi sampling. Multivariate logistic regression was used to analyze the association between acceptance of testing and the aforementioned predictors, controlling for potential confounders. Results: Nine hundred forty-nine (17.3%) of 5,490 patients underwent fragile X screening. We observed significant variation in uptake by genetic counselor. Additionally, women who had Medical/Medicaid insurance (aOR: 1.99; CI: 1.63 - 2.43), or who had amniocentesis or chorionic villi sampling (aOR: 2.48; CI: 1.99 - 3.08) had increased odds of undergoing fragile X screening. Conclusions: Numerous factors that are reported in patients’ charts are associated with decisions to undergo fragile X DNA molecular diagnosis. Interestingly, modifiable factors including the patient’s genetic counselor and insurance status appear to have a significant impact on acceptance of fragile X screening. J Clin Gynecol Obstet. 2015;4(2):203-208 doi: http://dx.doi.org/10.14740/jcgo301w
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".