A 2‐year review of publicly funded cell‐free DNA screening in Ontario: utilization and adherence to funding criteria
Bibliographic record
Abstract
OBJECTIVE: Ontario offers a publicly funded modified contingent model of prenatal screening for aneuploidy in which cell-free DNA (cfDNA) screening is covered for pregnancies at higher risk of fetal aneuploidy. The objective of this study was to review utilization of provincially funded cfDNA screening and adherence to the criteria laid out in Ontario prenatal screening guidelines. METHODS: This was a descriptive cohort study using data collected by Ontario's prescribed maternal and child registry. The study population included all pregnant individuals who received cfDNA screening from January 2016 to December 2017. RESULTS: The most common criteria for provincially funded cfDNA screening were advanced maternal age ≥40 years (37.7%), positive multiple marker screen (34.1%), modifying risk factors such as ultrasound soft markers (7.1%), and previous aneuploidy (5.5%). The audit demonstrated that 2.9% of funded cfDNA screens tests did not meet funding criteria, and that 11.4% of self-paid cfDNA screens could have been publicly funded. CONCLUSION: Reviewing and auditing the application of criteria for funded cfDNA screening using prescribed registry data allows an opportunity to identify areas where targeted education may improve adherence to standardized screening protocols, and provides a basis for reassessment of the funding model.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| 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) | 0.002 | 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".