Prevalence of malnutrition and impact on 30‐day hospital readmission in adults receiving home care and ambulatory care: A descriptive cohort study
Bibliographic record
Abstract
BACKGROUND: Little is known about the prevalence of malnutrition among patients receiving home care (HC) and ambulatory care (AC) services. Further, the risk of hospital readmission in malnourished patients transitioning from hospital to HC or AC is also not well established. This study aims to address these two gaps. METHODS: A descriptive cohort study of newly referred HC and AC patients between January and December 2019 was conducted. Nutrition status was assessed by clinicians using the Mini Nutritional Assessment-Short Form (MNA-SF). Prevalence of malnutrition and at risk of malnutrition (ARM) was calculated, and a log-binomial regression model was used to estimate the relative risk of hospital readmission within 30 days of discharge for those who were malnourished and referred from hospital. RESULTS: A total of 3704 MNA-SFs were returned, of which 2402 (65%) had complete data. The estimated prevalence of malnutrition and ARM among newly referred HC and AC patients was 21% (95% CI: 19%-22%) and 55% (95% CI: 53%-57%), respectively. The estimated risk of hospital readmission for malnourished patients was 2.7 times higher (95% CI: 1.9%-3.9%) and for ARM patients was 1.9 times higher (95% CI: 1.4%-2.8%) than that of patients with normal nutrition status. CONCLUSION: The prevalence of malnutrition and ARM among HC and AC patients is high. Malnutrition and ARM are correlated with an increased risk of hospital readmission 30 days posthospital discharge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".