Applications of Genetic Testing for Endocrine and Metabolic Disorders
Bibliographic record
Abstract
Knowledge of inherited diseases and the ability to rapidly, efficiently and comprehensively perform genetic testing are advancing steadily. However, the ideal approach to translate this ability into clinical applications for endocrine disorders has yet to be determined. This work focuses on aspects of clinically translating knowledge of select heritable endocrine and metabolic conditions.\nFor maturity onset diabetes of the young (MODY), a monogenic disorder with no current consensus guidelines governing testing procedures, this work addresses methods to improve detection by validating the use of next generation sequencing-based techniques to identify MODY cases and to detect copy number variations.\nFor very severe hypertriglyceridemia, a largely polygenic trait, this work explores clinical differences associated with the underlying genotype, assesses treatment of pancreatitis, the most severe acute complication of hypertriglyceridemia, and presents a population-based study of Ontario adults to identify the most important modifiable risk factors associated with expression of hypertriglyceridemia, and to identify any gaps in appropriate care for this population.\nFor heterozygous familial hypercholesterolemia, a condition for which universal genetic screening has been recommended, this work explores the personal impact of this diagnosis on the patient in terms of quality of life, lifestyle and self-care habits.\nThe ultimate goal of this project is to expand the available knowledge on how best to translate the laboratory ability and findings into the clinical realm for these select endocrine and metabolic conditions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".