The Impact of Chronic Disease on the Corrected QT (QTc) Value in Women in a British Columbia First Nations Population
Bibliographic record
Abstract
BACKGROUND: Indigenous women have higher rates of chronic disease than Indigenous men and non-Indigenous women. Long QT syndrome (LQTS) can be inherited or acquired; the latter may occur with chronic disease. A prolonged corrected QT value (QTc) is an independent risk factor for ventricular arrhythmias and sudden death, but few studies have quantified the impact of chronic disease on the QTc. We assessed the association between chronic disease and QTc prolongation in a population of First Nations women previously ascertained to study a high rate of inherited LQTS due to a unique genetic (founder) variant in their community. METHODS: This substudy focusing on women expands on the original research where patients with clinical features of LQTS and their relatives were assessed for genetic variants discovered to affect the QTc. Medical records were retrospectively reviewed and chronic diseases documented. Using multivariate linear regression, adjusting for the effect of genetic variants, age, and QTc-prolonging medications, we evaluated the association between chronic disease and the QTc. RESULTS: In total, 275 women were included. After adjustments, a prolonged QTc was associated with coronary artery disease (26.5 ms, 95% confidence interval [CI] 9.0-44.1 ms; P = 0.003), conduction system disease (26.8 ms, 95% CI 2.2-51.4 ms; P = 0.033), rheumatoid arthritis (28.9 ms, 95% CI 12.7-45.1 ms; P = 0.001), and type 2 diabetes mellitus (17.9 ms, 95% CI 3.6-32.3 ms; P = 0.015). CONCLUSIONS: This quantification of the association between chronic disease and QTc prolongation in an Indigenous cohort provides insight into the nongenetic determinants of QTc prolongation. Corroboration in other populations will provide evidence for generalisability of these results.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".