Salt reduction to prevent hypertension: the reasons of the controversy
Bibliographic record
Abstract
There is a causal relationship between dietary salt intake and blood pressure. A reduction in salt intake from the current world average of ∼10 g/day to the WHO recommended level of <5 g/day, lowers blood pressure and reduces the risk of cardiovascular disease and all-cause mortality. However, a few cohort studies have claimed that there is a J-shaped relationship between salt intake and cardiovascular risk, i.e. both high and low salt intakes are associated with an increased risk. These cohort studies have several methodological problems, including reverse causality, and inaccurate and biased estimation of salt intake, e.g. from a single spot urine sample with formulas. Recent studies have shown that the formulas used to estimate salt intake from spot urine cause a spurious J-curve. Research with inappropriate methodology should not be used to refute the robust evidence on the enormous benefits of population-wide reduction in salt intake. Several countries, e.g. Finland, the UK, have successfully reduced salt intake, which has resulted in falls in population blood pressure and deaths from stroke and ischaemic heart disease. Every country should develop and implement a coherent, workable strategy to reduce salt intake. Even a modest reduction in salt intake across the whole population will lead to a major improvement in public health, along with huge cost-savings to the healthcare service.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".