A Review of Newborn Screening Programs for Cystic Fibrosis: Are Current Protocols Appropriate for Canada's Diverse Population?
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of cystic fibrosis (CF) through newborn screening (NBS) programs has improved health outcomes in people with CF (pwCF). NBS programs can vary in specific protocols and genetic variants tested, which may not perform equitably for all infants. The objective of this study was to summarize the Canadian CF NBS programs to understand if there are any gaps that may drive inequities. METHODS: Details about each of the Canadian CF NBS programs were gathered by collating publicly available information and consulting directly with each program. The Canadian CF Registry (CCFR) was used to identify Canadians with CF in 2022, to estimate the proportion of individuals that would have been identified by each NBS program in Canada. RESULTS: All jurisdictions in Canada include CF in their NBS programs, which follow a similar multistep process: (1) evaluation of immunoreactive trypsinogen (IRT), (2) genetic testing of a predefined set of variants. Most jurisdictions analyzed IRT locally, whereas genetic testing was centralized to five programs. Applying the current NBS CFTR variant panels from each program to the 4445 individuals in the CCFR identified over 96% of the Canadian CF population. All variant screening panels were more likely to identify pwCF who were: (1) born before 2018, (2) diagnosed as children, and (3) described as White. INTERPRETATION: Canadian NBS panels would have captured over 96% of all people in the CCFR; however, they would fail to identify 12%-20% of non-White individuals. As Canada's population becomes more diverse, updates to NBS programs may be needed to ensure inequities in screening and diagnosis do not further widen.
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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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".