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Record W2091404211 · doi:10.1177/0115426502017005296

Weight‐Based Ordering: An Evaluation of Increased Guideline Use in Hospital Total Parenteral Nutrition Dosing

2002· article· en· W2091404211 on OpenAlexaffabout
Deonne Dersch, Judy Schoen

Bibliographic record

VenueNutrition in Clinical Practice · 2002
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsDosingMedicineParenteral nutritionCalorieAzotemiaInternal medicineRenal function

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.441
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2002
Admission routes2
Has abstractyes

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