Implementation of a Malnutrition Screening Risk Tool in The Stroke Prevention Clinic At Toronto Western Hospital To Identify Malnutrition Risk : a Quality Improvement Project
Bibliographic record
Abstract
Background Malnutrition post stroke is associated with increased mortality, reduced functional recovery and increased hospital readmission rates. The prevalence ranges from 6% to 62% in Canada. Dysphagia, fatigue, decreased functional status and cognitive deficits are some factors that increase the risk. The Canadian Stroke Best Practices recommendations include periodic malnutrition risk screening in the community for patients post stroke with a validated tool. Malnutrition is under recognized in this outpatient population. This Quality Improvement Project (QIP) addresses this gap by identifying patients at risk of malnutrition in the Stroke Prevention Clinic using the Malnutrition Screening Tool (MST). The subsequent provision of nutrition intervention optimizes nutritional status and contributes to recovery.Methodology The validated MST consisting of 2 questions regarding appetite and recent unintentional weight loss was selected. It was implemented in the Stroke Prevention Clinic from January 15th to February 26th, 2018. The RD Student or Registered Dietitian performed the screening and provided nutrition intervention as needed. Ethics approval was not required.Results In total, 99 patients were screened and 23 (23.2%) were at risk of malnutrition (50% male, 50% female). Of the 23 at risk, 17 (73.9%) expressed interest in nutrition intervention.ConclusionThis QIP provides a novel interprofessional collaboration opportunity, identifying patients at risk of malnutrition in the Stroke Prevention Clinic. It is essential that stroke prevention strategies target malnutrition to improve outcomes. The literature and results demonstrate the need and value for routine implementation of the MST and nutrition intervention in the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".