Screening and assessment of nutritional status following stroke: results from a national survey of registered dietitians in Canada
Bibliographic record
Abstract
PURPOSE: The objective of this study was to survey registered dietitians (RDs) at acute care hospitals across Canada to determine whether valid screening and assessment methods (i.e. those with formal evaluation of the psychometric and/or clinimetric properties of the tool) are used to identify protein-energy malnutrition following stroke. METHODS: One hundred and twenty-five Canadian healthcare institutions admitting high volumes (≥100/year) of acute stroke patients were identified. RDs at these sites who provided services to patients recovering from acute stroke were contacted and invited to participate in an online survey. RESULTS: Ninety-five RDs completed the survey. Sixty dietitians (63%) indicated that patients were screened routinely at their institution, which was conducted primarily by dietitians (n = 64, 67.3%). Ten respondents (10.5%) stated they used a screening tool with previously-established validity, while 32 (33.7%) indicated they used a validated assessment tool. Among dietitians using validated tools, 40% and 64% indicated that they used modified versions of the original screening and assessment tools. The most frequently cited tools used for both screening and assessment purposes were Subjective Global Assessment and Mini Nutritional Assessment. CONCLUSIONS: The results of this national survey suggest that screening and assessment of nutritional status following acute stroke is conducted using tools that have not been validated previously. Implications for Rehabilitation Although the use of valid screening and assessment tools is considered part of evidence-based practice, and recommendations for their adoption are included in several national stroke-specific clinical practice guidelines, the results from an online survey suggest that they are not used in clinical practice by RDs in Canada caring for patients following stroke. There are associated risks with the use of screening and assessment tools, which have not been formally validated. The true nutritional status of patients may not be initially identified correctly, and the response to subsequent nutritional interventions may not be assessed accurately or reliably. While the results of this survey were stroke-specific, we suggest that the results are likely generalizable to dietitians working in other patient areas.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; 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".