Which Reported Estimate of the Prevalence of Malnutrition After Stroke Is Valid?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The reported prevalence of malnutrition after stroke varies widely, whereas it remains unclear which of the estimates is most accurate. The aim of this review was to explore possible sources of this heterogeneity among studies and to evaluate whether the nutritional assessment techniques used were valid. METHODS: A literature search was conducted to identify all studies in which the nutritional state of patients was assessed after inpatient admission for stroke. The percentages of patients identified as malnourished in each study and method of nutritional assessment are reported. For the purposes of this study, an assessment technique was considered valid if at least one form of validity had been demonstrated previously through psychometric evaluation. RESULTS: Eighteen studies meeting inclusion criteria were identified. The reported frequency of malnutrition ranged from 6.1% to 62%. Seventeen different methods of nutritional assessment were used. Four trials used previously validated assessment methods: Subjective Global Assessment, "an informal assessment," and Mini Nutritional Assessment. The nutritional assessment methods used in the remaining studies used had not been validated previously. CONCLUSIONS: The use of a wide assortment of nutritional assessment tools, many of which have not been validated, may have contributed to the wide range of estimates of malnutrition. If so, this underscores the need for valid and reliable assessment tools to further our understanding of the relationship between stroke and nutritional status.
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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 | 0.019 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".