Estimated inpatient malnutrition prevalence, screening tool utilization, and dietitian referral rates across hospitals during extension of phase 2 of More-2-Eat
Bibliographic record
Abstract
Malnutrition is associated with increased hospital length of stay, disease burden, and healthcare costs. The Integrated Nutrition Pathway for Acute Care (INPAC) is a validated multi-step algorithm that includes screening using the Canadian Nutrition Screening Tool (CNST) and diagnosis using Subjective Global Assessment (SGA). This study aims to understand ( 1) the prevalence of inpatient nutrition screening completion, (2) the proportion of at-risk inpatients referred to dietitians, and (3) the malnutrition prevalence among a sample of hospital inpatients. In 2021, INPAC was implemented in five hospital wards across Nova Scotia (NS) as an extension of More-2-Eat NS Study. As part of the implementation, hospital chart audits (n=672) were completed from 2021-2022 to gather data on malnutrition screening, dietitian referral, and nutrition assessment. Statistical analysis involved chi-square, Kruskal Wallis, and t-tests. Nutrition screening at admission occurred for 54.9% of audited patients, with variation among sites (p<0.001). 34.5% of these screened patients were at nutritional risk, of whom 79.8% were referred to a dietitian. 14.4% of all charts audited had a malnutrition diagnosis as per SGA, as did 28.5% of patients screened by the CNST. 94.2% of patients who underwent SGA were diagnosed with malnutrition. Inpatient malnutrition is prevalent in NS hospitals but under-diagnosed due to gaps in screening. INPAC implementation increased dietitian referrals, SGA, and malnutrition diagnosis. Investigation is needed to assess and overcome barriers to screening, consequences to clinician workload, and the burden of malnutrition on prognosis and hospital stay.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".