The Science of Salt: A regularly updated systematic review of the implementation of salt reduction interventions (September 2016–February 2017)
Bibliographic record
Abstract
This periodic review aims to identify, summarize, and appraise studies relating to the implementation of salt reduction strategies that were published between September 2016 and February 2017. A total of 41 studies were included as relevant to the design, assessment, and implementation of salt reduction strategies, and a detailed appraisal was conducted on the seven studies that evaluated the impact of salt reduction strategies. Of these, three were national studies or included large populations and four were conducted in communities with small participant sample sizes. Each study used a different strategy for reducing salt intake varying from category‐specific sodium targets for packaged food to use of a low‐sodium salt substitute to behavior change interventions. Four studies found statistically significant decreases in dietary salt intake and one study showed statistically significant decreases in mean sodium density of packaged food products. Four of the seven studies used either spot or 24‐hour urine samples to measure dietary salt intake and five were conducted in East or Southeast Asia—two of which were in low‐ and middle‐income countries. Study quality varied among the seven studies and all except one had one or more risks related to bias.
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 | 0.031 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.028 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".