Measuring elderly dysphagic patients' performance in eating – a review
Bibliographic record
Abstract
PURPOSE: This review aims to identify psychometrically robust assessment tools suitable for measuring elderly dysphagic patients' performance in eating for use in clinical practice and research. METHOD: Electronic databases, related citations and references were searched to identify assessment tools integrating the complexity of the eating process. Papers were selected according to criteria defined a priori. Data were extracted regarding characteristics of the assessment tools and the evidence of reliability, validity and responsiveness. Quality appraisal was undertaken using developed criteria concerning the study design, the statistics used for the psychometric evaluation and the reported values. RESULTS: Eight of fourteen identified assessment tools met the inclusion criteria. Three assessment tools were specific to dementia, two were specific to stroke and three targeted a range of neurological and geriatric conditions. The rigor of the assessment tools' psychometric properties varied from no evidence available to excellent evidence. Only two assessment tools were rated adequate to excellent. CONCLUSION: 'The Minimal Eating Observation Form-Version II' to be used for screening and 'The McGill Ingestive Skills Assessment' to be used for treatment planning and monitoring appeared to be psychometrically robust for clinical practice and research. However, further research on their psychometric properties is needed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".