Sodium levels in hospital patient menus exceed recommended levels
Bibliographic record
Abstract
One strategy to lower population sodium intakes is to develop food procurement policies for publically funded institutions such as hospitals. It is unknown if hospital patient menus contain sodium levels that fall within recommendations. We analyzed hospital patient menus to quantify sodium levels and to determine if these are in agreement with sodium recommendations. We evaluated menus for four diet prescriptions: Regular and Diabetic diets, and 3g‐Sodium and 2g‐Sodium therapeutic sodium diets. We included 84 standard, unselected patient menus and 2234 patient‐selected menus from three acute care hospitals. The sodium content of the Regular, Diabetic, 3g and 2g sodium menus was 2.9±0.6, 3.4 ± 0.5, 2.4±0.4, and 1.5±0.3 g/day for the standard, unselected menus; and 3.0±0.9, 3.6±0.9, 2.5±0.7, and 2.0±0.9 g/day for the patient‐selected menus, respectively. More than 76% of patient‐selected and 86% of standard Regular menus, and 95% of patient‐selected and 100% of standard Diabetic menus exceeded the tolerable upper level (UL) of 2300 mg sodium/day. Most therapeutic sodium menus met prescribed levels; however a higher proportion of menus exceeded the UL when patients self‐selected their menu (p<0.001). In conclusion, the majority of hospital menus contain sodium levels exceeding recommendations, suggesting that hospitals and hospital food manufacturers should participate in sodium‐reduction initiatives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".