Weight‐Based Ordering: An Evaluation of Increased Guideline Use in Hospital Total Parenteral Nutrition Dosing
Bibliographic record
Abstract
BACKGROUND: In the past, parenteral nutrition in the Calgary Health Region was ordered as volumes of standard solutions, which limited individualization. Ordering total parenteral nutrition (TPN) that falls within macronutrient dosing guidelines may minimize complications associated with TPN, such as hyperglycemia, azotemia, hepatic steatosis, or continued malnutrition and catabolism. The Foothills Medical Centre in Calgary changed to a weight-based ordering system for TPN in 1999. This study's purpose was to determine if this change increased adherence to TPN dosing guidelines. METHODS: Macronutrient doses in TPN solutions ordered as standard solutions were compared with those ordered by weight. Mean protein, dextrose, lipid, and kilocalorie doses and the number of orders deviating from guidelines were examined. RESULTS: Weight-based dosing showed a significant reduction in deviation from guidelines for kilocalorie dose compared with TPN ordered as standard solutions. There also was a significant increase in mean protein dose and reductions in mean dextrose load and mean kilocalorie dose in the weight-based TPN group only, suggesting these changes were caused by the change in ordering method. CONCLUSIONS: Overall, weight-based ordering increased adherence to TPN dosing guidelines. The study did not have the statistical power to show significant differences between weight-based or standard TPN dosing; however, several trends were shown.
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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 | 0.017 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".