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Record W2072736947 · doi:10.1007/s10545-006-0118-1

A study on the nature of genetic metabolic practice at a major paediatric referral centre

2006· article· en· W2072736947 on OpenAlexaffabout
Hannah C. Glass, Annette Feigenbaum, Joe T.R. Clarke

Bibliographic record

VenueJournal of Inherited Metabolic Disease · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsReferralMedical diagnosisMedicineMedical geneticsSick childGenetic diagnosisPediatricsMetabolic diseaseGenetic testingHuman geneticsFamily medicineIntensive care medicinePathologyGeneticsBiologyInternal medicine

Abstract

fetched live from OpenAlex

A retrospective chart review of new paediatric patients seen during the calendar year 1998 by specialists of the Division of Clinical and Metabolic Genetics of the Hospital for Sick Children in Toronto, the largest such referral centre in the country, showed that 81% of specific genetic metabolic diagnoses were made within one month of being seen in consultation by one of the consultants of the programme. In 5% of cases, a specific diagnosis was not reached 4 years after initial consultation. We concluded from this study that the specific diagnosis of inborn errors of metabolism at a major medical genetic referral centre tended to be made quickly, or never. Some of the causes of delays in diagnosis include (1) the lack of ready access to existing diagnostic laboratory testing; (2) technical barriers to the identification of specific metabolic or genetic defects; and (3) incomplete knowledge of genetic defects causing inherited metabolic 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 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)0.001
metaresearch head score (Gemma)0.009
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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

Citations6
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Inherited Metabolic DiseaseSame topicMetabolism and Genetic DisordersFrench-language works237,207