Elliott et al. Respond to "Quantifying Urine Sodium Excretion"
Bibliographic record
Abstract
In their commentary (1) on our article (2), de Boer and Kestenbaum briefly summarized recent research on the relationship between salt intake (estimated from urinary sodium excretion) and cardiovascular disease (CVD). They cite a recent article by O'Donnell et al. (3) in which casual urine samples were used to characterize individual sodium intakes and an apparent J-shaped association was observed between sodium intake and CVD risk. de Boer and Kestenbaum state that “a very low intake of dietary sodium may truly increase the risk of CVD” (1, p. 1193); in contrast, an editorial (4) accompanying the article by O'Donnell et al. and subsequent publications (5–7) have highlighted methodological concerns about interpretation of the study findings. These include the use of a clinical trial population with established CVD or diabetes; high rates of medication usage among trial participants; use of a single casual urine sample to estimate individual sodium intake; inclusion of participants with sodium intakes at the bottom end of the distribution who appeared to be sicker than the rest of the study population (reverse causality); and use of data sets not specifically designed to address the sodium-CVD relationship.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".