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Are patients identified to be at risk for malnutrition being seen by a dietitian upon admission to hospitalist medicine units at Vancouver General Hospital?

2018· preprint· en· W4213151682 on OpenAlexaffabout
Sharon Voong, Theresa Cividin, Jasmine Sookero

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsMalnutritionMedicineHospital medicineHospital admissionFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Malnutrition is under recognized and often undiagnosed in the hospital setting. Malnutrition contributes to increased mortality, morbidity, hospital admissions, length of hospital stay, healthcare costs and reduced quality of life. Dietitian involvement is critical in addressing malnutrition and its associated risk factors. Patients admitted to Vancouver General Hospital (VGH) are screened for malnutrition risk using a validated nutrition screening tool (Canadian Nutrition Screening Tool) within the Nursing Admission Assessment (NAA).Objectives: The objectives of this study are (1) to determine the proportion of patients who are screened by and determined to be at risk for malnutrition upon admission to 3 hospitalist medicine units at VGH and (2) to determine the proportion of those patients seen by a dietitian. Methods: A retrospective chart review of all patients admitted to VGH hospitalist medicine units from March 1-31, 2017 was conducted. Data collected included demographics, completion of the nutrition screen within the NAA, dietitian referrals and assessments. Results: Of the 161 patients admitted, 51% (n=82) were screened for malnutrition upon admission. Of these patients, 24% (n=20) were determined to be at malnutrition risk. Despite only 15% (n=3) of these patients being referred to a dietitian, 50% (n=10) of patients at malnutrition risk were seen by a dietitian. The main reasons for dietitian assessment were physician referrals and dietitian routine screening. Conclusions: The NAA was not consistently completed and patients at risk were not always referred to the dietitian. Suggestions to address these issues include identifying barriers for completing the NAA and providing nursing education about the importance of malnutrition screening, use of the tool, and how to refer to the dietitian.Significance to the field of dietetics: Consistent completion of the validated malnutrition screening tool and resulting dietitian referrals could help identify malnutrition early and decrease routine screening time for dietitians.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.330
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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