The Use of Genetic Testing Panels for Dyslipidemia: A Quality Improvement Project at the McGill University Health Centre
Bibliographic record
Abstract
Abstract Background: Genetic testing panels are used to identify the most common genetic causes of dyslipidemia, and the results of these panels can guide treatment and management. The objective of this quality improvement project was to assess the appropriateness of genetic testing panels requested by the McGill University Health Centre (MUHC). Methods: Genetic testing panels sent for analysis from January 2018 to December 2019 were identified. Ordering physician specialty, patient personal and family medical history, lipid panel results, and genetic testing results were collected. Then, validated Familial Hyperlipidemia (FH) scores (Simon-Broome Registry Criteria, Dutch Lipid Clinic Network Criteria, FH Canada criteria) were calculated for patients who underwent genetic testing for suspected FH. Results: There were 36 genetic test panels sent out for analysis during the study period, of which 24 were accessible for data analysis. Pathogenic mutations were identified in 7/24 (29%) of the analyzed panels. The 19/24 (79%) of the panels were requested by lipid specialists, and all of the panels positive for pathogenic mutations were requested by lipid specialists. Interestingly, 23/24 (94%) of the patients met the Canadian criteria for at least considering genetic testing, suggesting that most panels were appropriately requested. Only 3/24 (12%) of patients had insufficient criteria for FH by the Simon-Broome criteria, but all of these carried pathogenic mutations. Conclusion: These results suggest that at the MUHC, using the Canadian criteria identifies a greater number of patients for genetic testing and for appropriate diagnosis and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".