Declining invasive prenatal diagnostic procedures: A comparison of tertiary hospital and national data from 2012 to 2015
Bibliographic record
Abstract
BACKGROUND: In recent years, the superior accuracy of maternal plasma cell-free DNA-based prenatal screening has resulted in >50% national decline in amniocenteses and chorionic villus sampling (CVS), creating new implications for specialist training. OBJECTIVE: To compare the annual figures on amniocenteses and CVS in a tertiary hospital with national population-based trends between 2012 and 2015. METHODS: Retrospective study examining the amniocentesis and CVS procedures performed in a tertiary hospital between 2012 and 2015. Numbers of procedures, indications for testing, type of test and diagnostic results were analysed. Trends in the annual numbers of procedures were compared to national population-based data from Medicare Benefits Schedule database. RESULTS: The annual numbers of diagnostic procedures in our tertiary centre fell from 267 to 215 over the study period, representing a 19.5% decline. This was significantly smaller than the corresponding national decline of 53.7% for the same period (P < 0.0001). In 2015, ultrasound abnormality (including nuchal translucency ≥ 3.5 mm) surpassed high-risk screening results as the most common indication for invasive testing. Thirty percent of procedures performed for an ultrasound abnormality occurred prior to 18 weeks gestation. CONCLUSION: Our tertiary centre experienced a relatively smaller decline in prenatal diagnostic procedures compared with national figures, largely due to an increase in testing for ultrasound abnormalities. Our results demonstrate the increasing contribution of first trimester ultrasound in the detection of fetal abnormalities in the cell-free DNA era and the continued viability of specialist training in invasive procedures.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".