Estimated sodium intake and premature ventricular complexes: data from the population-based Swedish CArdioPulmonary bioImage Study
Bibliographic record
Abstract
BACKGROUND: Premature ventricular complexes (PVCs) predict coronary heart disease, heart failure, atrial fibrillation and death, all of which are also related to sodium intake. We studied estimated sodium intake and PVC frequency in the randomly selected population-based Swedish CArdioPulmonary bioImage Study cohort. METHODS: In our cross-sectional study, we included 5636 individuals with 24-hour ECG registration and fasting morning urine sampling. Sodium intake was estimated using the Kawasaki formula, and the association between sodium intake and PVC frequency was modelled using multivariable negative binomial regression, adjusted for age, sex, body mass index, level of education, height, physical activity and smoking status, across prespecified strata of sodium intake: <2 g/day, 2-2.99 g/day, 3-3.99 g/day (reference category), 4-4.99 g/day and ≥5 g/day. RESULTS: The median age was 57.6 years, and 51.9% were female. The median daily PVC count was 8 (IQR 3-41); 5.9% had ≥500 PVCs/24 hours. The mean estimated sodium intake was 3.3 g/day. There was a U-shaped association between sodium intake and PVCs. Compared with the reference of 3-3.99 g/day (28% of participants), sodium intakes <2 g/day (15% of participants) and ≥5 g/day (10% of participants) were associated with 26% (95% CI 6% to 49%) and 52% (95% CI 26% to 84%, p<0.01) increases in PVC frequency, respectively, but intakes of 2-2.99 g/day and 4-4.99 g/day were not (5% (95% CI -8% to 20%) and 4% (95% CI -11% to 22%) increase, respectively). CONCLUSION: There was a U-shaped association between sodium intake and PVC frequency, with both low and high sodium intake associated with higher PVC frequency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".