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Record W2050180123 · doi:10.1542/peds.105.1.e10

Incidence of Inborn Errors of Metabolism in British Columbia, 1969–1996

2000· article· en· W2050180123 on OpenAlexaffabout
Derek A. Applegarth, Jennifer R. Toone, RT § and R. Brian Lowry

Bibliographic record

VenuePEDIATRICS · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineIncidence (geometry)PopulationPediatricsReferralLysosomal storage disordersGlycogen storage diseaseMedical recordMedical diagnosisDiseasePathologyFamily medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine how many children with specific types of inborn errors of metabolism are born each year in British Columbia, Canada. This population provides a relatively unique setting for collection of accurate and uniform incidence data because the diagnoses are all made through one laboratory in a population with universal access to government-funded medical care. METHODOLOGY: We used the records of the Biochemical Diseases Laboratory, Children's Hospital, Vancouver (the central referral point for all metabolic diagnoses in British Columbia) to identify all patients diagnosed with the metabolic diseases defined below. We obtained incidence figures by including only the children diagnosed with the diseases covered in this article who were confirmed as having been born within the province for the years 1969 to 1996. The diseases covered were diseases of amino acids, organic acids, the urea cycle, galactosemia, primary lactic acidoses, glycogen storage diseases, lysosomal storage diseases, and diseases involving specifically peroxisomal and mitochondrial respiratory chain dysfunction. Because the technology needed for diagnosis of specific disease groups was in place at different times our data for the different disease groups correspond to different time frames. We have also adjusted the time frames used to allow for the likelihood that some diseases may not come to medical attention for some time after birth. For instance the incidence of amino acid diseases was assessed throughout the whole of this time frame but the incidence of peroxisomal diseases was restricted to 1984 to 1996 because this was the time frame during which the technology needed for diagnosis was in place and reliable. Most disease group statistics included at least 400 000 births. RESULTS: The overall minimum incidence of the metabolic diseases surveyed in children born in British Columbia is approximately 40 cases per 100 000 live births. This includes phenylketonuria (PKU) and galactosemia which are detected by a newborn screening program. Metabolic diseases, which were not screened for at birth, ie, those with PKU and galactosemia subtracted from the total, have a minimal incidence of approximately 30 cases per 100 000 live births. This diagnostic dilemma group would present to pediatricians for diagnosis. Not all metabolic diseases have been surveyed and our data are restricted to the following metabolic disease groups. Approximately 24 children per 100 000 births (approximately 60% of the total disease groups surveyed) have a disease involving amino acids (including PKU), organic acids, primary lactic acidosis, galactosemia, or a urea cycle disease. These children all have metabolic diseases involving small molecules. Approximately 2.3 children per 100 000 births ( approximately 5%) have some form of glycogen storage disease. Approximately 8 per 100 000 births (20%) have a lysosomal storage disease; approximately 3 per 100 000 births (7%-8%) have a respiratory chain-based, mitochondrial disease and approximately 3 to 4 per 100 000 (7%-8%) of births have a peroxisomal disease. The diseases involving subcellular organelles represent approximately half of the diagnostic dilemma group. The incidence of each of the specific diseases diagnosed, including apparently rare diseases such as nonketotic hyperglycinemia, is to be found in the text. The metabolic diseases reported in this survey represent over 10% of the total number of single gene disorders in our population. CONCLUSIONS: Our data provide a good estimate of metabolic disease incidence, for the disease groups surveyed, in a predominantly Caucasian population. Incidence data for metabolic diseases are hard to collect because in very few centers are diagnoses centralized for a population with uniform access to modern health care and this has been the case for our population during the course of the study. (ABSTRACT TRUNCATED)

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.004
GPT teacher head0.212
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations420
Published2000
Admission routes2
Has abstractyes

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