Performance of a Three-Tier (IRT-DNA-IRT) Cystic Fibrosis Screening Algorithm in British Columbia
Bibliographic record
Abstract
Newborn screening for Cystic Fibrosis has been implemented in most programs worldwide, but the approach used varies, including combinations of immunoreactive trypsinogen (IRT) and CFTR mutation analysis on one or more specimens. The British Columbia (BC) newborn screening program tests ~45,000 infants per year in BC and the Yukon Territory, covering almost 1.5 million km2 in western Canada. CF screening was initiated using an IRT-DNA-IRT approach with a second bloodspot card at 21 days of age for all CFTR mutation heterozygotes and any non-carriers in the top 0.1% for IRT. This second IRT was implemented to avoid sweat testing of infants without persistent hypertrypsinemia, reducing the burden of travel for families. Over nine years (2010–2018), 401,977 infants were screened and CF was confirmed in 76, and a further 28 were deemed CF screen positive inconclusive diagnosis (CFSPID). Day 21 IRT was normal in 880 CFTR mutation carriers who were quoted a very low CF risk and offered optional sweat testing. Only 13% of families opted for sweat testing and a total of 1036 sweat tests were avoided. There were six false negative CF cases (and three CFSPID) due to a low initial IRT or no CFTR mutations. Although one CFSPID case had a normal repeat IRT result, the addition of the day 21 IRT did not contribute to any CF false negatives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".