The health system impact of false positive newborn screening results for medium-chain acyl-CoA dehydrogenase deficiency: a cohort study
Bibliographic record
Abstract
BACKGROUND: There is no consensus in the literature regarding the impact of false positive newborn screening results on early health care utilization patterns. We evaluated the impact of false positive newborn screening results for medium-chain acyl-CoA dehydrogenase deficiency (MCADD) in a cohort of Ontario infants. METHODS: The cohort included all children who received newborn screening in Ontario between April 1, 2006 and March 31, 2010. Newborn screening and diagnostic confirmation results were linked to province-wide health care administrative datasets covering physician visits, emergency department visits, and inpatient hospitalizations, to determine health service utilization from April 1, 2006 through March 31, 2012. Incidence rate ratios (IRRs) were used to compare those with false positive results for MCADD to those with negative newborn screening results, stratified by age at service use. RESULTS: We identified 43 infants with a false positive newborn screening result for MCADD during the study period. These infants experienced significantly higher rates of physician visits (IRR: 1.42) and hospitalizations (IRR: 2.32) in the first year of life relative to a screen negative cohort in adjusted analyses. Differences in health services use were not observed after the first year of life. CONCLUSIONS: The higher use of some health services among false positive infants during the first year of life may be explained by a psychosocial impact of false positive results on parental perceptions of infant health, and/or by differences in underlying health status. Understanding the impact of false positive newborn screening results can help to inform newborn screening programs in designing support and education for families. This is particularly important as additional disorders are added to expanded screening panels, yielding important clinical benefits for affected children but also a higher frequency of false positive findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".