It is strongly recommended to not conduct, fund, or publish research studies that use spot urine samples with estimating equations to assess individuals’ sodium (salt) intake in association with health outcomes: a policy statement of the World Hypertension League, International Society of Hypertension and Resolve to Save Lives
Bibliographic record
Abstract
Spot urine samples with estimating equations have been used to assess individuals' sodium (salt) intake in association with health outcomes. There is large random and systematic error in estimating sodium intake using this method and spurious health outcome associations. Substantial controversy has resulted from false claims the method is valid. Hence, the World Hypertension League, International Society of Hypertension and Resolve to Save Lives, supported by 21 other health organizations, have issued this policy statement that strongly recommends that research using spot urine samples with estimating equations to assess individuals' sodium (salt) intake in association with health outcomes should not be conducted, funded or published. Literature reviews on the health impacts of reducing dietary sodium that include studies that have used spot and short duration timed urine samples with estimating equations need to explicitly acknowledge that the method is not recommended to be used and is associated with spurious health outcome associations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.444 | 0.669 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.063 | 0.049 |
| Insufficient payload (model declined to judge) | 0.018 | 0.043 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".