Hospital Patients Are Not Eating Their Full Meal: Results of the Canadian 2010–2011 nutritionDay Survey
Bibliographic record
Abstract
nutritionDay is a 1-day cross-sectional survey identifying how nutrition care is provided. This paper provides results of the first 2 Canadian nutritionDay surveys. In November 2010 and 2011, data from standardized questionnaires were collected from 193 units in Canadian hospitals consisting of unit demographics and patient information including weight history, health status, nutrition assessment, nutrition therapy, food intake and 30-day outcomes. Results indicated that overall, 46% of the 1905 patients reported weight loss in the previous 3 months, and in half of these it was greater than 5 kg. Only 50% of the units had nutrition teams and nutrition therapy was provided to less than 14% of patients. More than 50% of patients ate less than normal in the previous week and 57% ate less than half of their meal on nutritionDay. Within the next 30 days the majority of patients went home, 10% remained in hospital, and 6% were readmitted. In this study, nutritionDay provided relevant information on nutrition assessment, weight history, food intake, nutrition therapy, length of stay, and outcomes in participating Canadian institutions. Data from 2010 and 2011 can help to both reflect on current practices and define continuous improvements through benchmarking with the overall goal of mitigating suboptimal nutrition intake during hospitalization.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".