Comparison between two newborn screening strategies for cystic fibrosis in Argentina: IRT/IRT vs. IRT/PAP
Bibliographic record
Abstract
Background: Benefits of early Cystic Fibrosis (CF) detection using newborn screening (NBS) lead to widespread use in NBS programs. Since 2002, a two-stage immunoreactive trypsinogen (IRT/IRT) screening strategy has been used as CFNBS method in all public maternities in the City of Buenos Aires, Argentina. However, novel screening strategies may be more efficient. The aim of the study is to prospectively compare two CFNBS strategies, IRT/IRT and IRT/PAP (pancreatitis-associated protein). Methods: A two-year prospective study was performed. IRT was measured in dried blood samples collected 48–72 hours after birth. When IRT value was abnormal, PAP was determined, and a second visit was scheduled to obtain another sample for IRT before 25 days of life. Newborns with a positive CFNBS were referred for confirmatory sweat test. Results: There were 69,827 births in the City of Buenos Aires during the period studied; 918 (1.31%) had an abnormal IRT. A total of 207 children (22.5%) failed to return for the second IRT, but only two PAP (0.2%) were not performed. IRT/IRT was more likely to lead to a referral for sweat testing than IRT/PAP (OR 2.3 [95% CI 1.8;2.9], p<0.001). Sensitivity, specificity, positive predictive value, and negative predictive value were: 80% and 100%, 86.5% and 82.6%, 4.04% and 4.2%, 99.84% and 100% for IRT/IRT and IRT/PAP strategies, respectively. Conclusion: The IRT/PAP strategy is more sensitive than IRT/IRT; it avoids a second appointment and the need of unnecessary sweat testing, and decreases loss to follow up in our population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".