Inborn errors of metabolism in newcomer and refugee populations in Ontario, CA
Bibliographic record
Abstract
Inborn errors of metabolism (IEM) are a heterogenous group of rare, inherited disorders that impair the biochemical processes involved in metabolism. Many are treated with various restrictive diets in early infancy, prior to the appearance of symptoms to improve the overall outcomes of the affected individuals. Identification of individuals at a risk of developing metabolic disorders through newborn screening (NBS) programs and the subsequent early diagnosis and treatment are an invaluable aspect of healthcare in Canada. Incorporation of Canada’s expanding population of refugees and new immigrants presents with potential challenges and changes. Without the availability of NBS programs in many countries contributing to the refugee influx in Canada, it may be difficult to identify patients who are affected with these rare conditions. This article discusses: 1) the utility of newborn screening and diagnosis of metabolic diseases in immigrant and refugee populations, with complex medical presentations, and 2) a recent diagnosis of succinic semialdehyde dehydrogenase deficiency (an IEM) in a refugee child past the age limit of NBS. An increasingly complicated and diverse refugee population requires a need for revision of existing healthcare practices pertaining to the diagnosis of rare diseases.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".