False-Positive Newborn Screening for Cystic Fibrosis and Health Care Use
Bibliographic record
Abstract
OBJECTIVES: Evidence is mixed regarding the impact of false-positive (FP) newborn bloodspot screening (NBS) results on health care use. Using cystic fibrosis (CF) as an example, we determined the association of FP NBS results with health care use in infants and their mothers in Ontario, Canada. METHODS: = 6256) born between April 2008 and November 2012 using linked health administrative data. Outcomes included maternal and infant physician and emergency visits and inpatient hospitalizations from the infant's third to 15th month of age. Negative binomial regression tested associations of NBS status with outcomes, adjusting for infant and maternal characteristics. RESULTS: A greater proportion of infants with FP results had >2 outpatient visits (16.2% vs 13.2%) and >2 hospital admissions (1.5% vs 0.7%) compared with controls; CF-related admissions and emergency department visits were not different from controls. Differences persisted after adjustment, with higher rates of outpatient visits (relative risk 1.39; 95% confidence interval 1.20-1.60) and hospital admissions (relative risk 1.67; 95% confidence interval 1.21-2.31) for FP infants. Stratified models indicated the effect of FP status was greater among those whose primary care provider was a pediatrician. No differences in health care use among mothers were detected. CONCLUSIONS: Higher use of outpatient services among FP infants may relate to a lengthy confirmatory testing process or follow-up carrier testing. However, increased rates of hospitalization might signal heightened perceptions of vulnerability among healthy infants.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".