Incidence, predictors and patterns of care of patients with very severe hypertriglyceridemia in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The incidence of severe (S-HTG) and very severe hypertriglyceridemia (VS-HTG) among Canadians is unknown. This study aimed to determine the incidence, characteristics, predictors and care patterns for individuals with VS-HTG. METHODS: Using linked administrative healthcare databases, a population-based cohort study of Ontario adults was conducted to determine incidence of new onset S-HTG (serum triglycerides (TG) > 10-20 mmol/L) and VS-HTG (TG > 20 mmol/L) between 2010 and 2015. Socio-demographic and clinical characteristics of those with VS-HTG were compared to those who had no measured TG value > 3 mmol/L. Univariable and multivariable logistic regression were used to determine predictors for VS-HTG. Healthcare patterns were evaluated for 2 years following first incidence of TG > 20 mmol/L. RESULTS: Incidence of S-HTG and VS-HTG in Ontario was 0.16 and 0.027% among 10,766,770 adults ≥18 years and 0.25 and 0.041% among 7,040,865 adults with at least one measured TG, respectively. Predictors of VS-HTG included younger age [odds ratios (OR) 0.64/decade, 95% confidence intervals (CI) 0.62-0.66], male sex (OR 3.83; 95% CI 3.5-4.1), diabetes (OR 5.38; 95% CI 4.93-5.88), hypertension (OR 1.69; 95% CI 1.54-1.86), chronic liver disease (OR 1.71; 95% CI 1.48-1.97), alcohol abuse (OR 2.47; 95% CI 1.90-3.19), obesity (OR 1.49; 95% CI 1.13-1.98), and chronic kidney disease (OR 1.39; 95% CI 1.19-1.63). CONCLUSION: The 5-year incidence of S-HTG and VS-HTG in Canadian adults was 1 in 400 and 1 in 2500, respectively. Males, those with diabetes, obese individuals and those with alcohol abuse are at highest risk for VS-HTG and may benefit from increased surveillance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".