THE EPIDEMIOLOGY AND HEALTH SYSTEM IMPACT OF MEDIUM-CHAIN ACYL-COA DEHYDROGENASE DEFICIENCY AMONG AFFECTED CHILDREN AND THOSE WITH FALSE POSITIVE NEWBORN SCREENING RESULTS IN ONTARIO, CANADA
Bibliographic record
Abstract
Introduction Limited research has focused on the health system impact of rare genetic disorders, including inborn errors of metabolism (IEM). Investigating patterns of health services use and their association with social and geographic characteristics is important for understanding the burden of disease, including the impact of screening and clinical management; and for identifying potential inequities in access to care. Objectives We conducted an observational study to provide a comprehensive description of the epidemiology and health system impact of IEM in Ontario, beginning with medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Methods The study cohorts consist of Ontario infants diagnosed with MCADD following a positive newborn screening result from April 2006 through March 2010 (n=45); and those who received a false positive screening result for MCADD during the same time period (n=51). The primary control population includes a random sample of infants with negative screening results. Two distinct secondary control groups were created by matching to the truly affected cohort and the false-positive cohort, respectively. Screening and confirmatory testing results are securely linked at the individual level with population-based administrative databases encompassing health services use (physician visits, hospitalizations, and emergency department care) for all insured Ontarians from April 2006 through March 2012. Based on the results of a literature review we conducted to identify existing approaches to addressing statistical reliability with small sample sizes, analyses are largely descriptive. Results We will describe: (i) the patterns of health care services use overall and by sociodemographic and geographic characteristics, (ii) the associations between types of service use and proxy health outcomes, and (iii) the costs of care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".