The discriminative validity of the McGill Ingestive Skills Assessment (MISA) /
Bibliographic record
Abstract
Introduction: Stroke is associated with a high prevalence of dysphagia in the elderly population. Hence, dysphagia evaluation and management are key issues in stroke rehabilitation. The McGill Ingestive Skills Assessment (MISA) is a recently developed mealtime observational tool aimed at evaluating the functional aspects of the oral phase of ingestion. Objective: To determine the discriminative validity of the MISA by assessing known/extreme groups of elderly individuals presenting with stroke, who have been admitted to an acute-care-hospital or a rehabilitation center. Participants were allocated to one of two groups: 1) individuals with stroke and no dysphagia, who are on a regular diet and 2) individuals with stroke and dysphagia, who are permitted only purees. Methods: Participants were evaluated with the MISA and a comprehensive chart review was conducted. Analysis: Groups were compared on socio-demographic and clinical characteristics. Univariate tests were performed to test the significance of between-group differences. Conclusion and significance: The results of the study are satisfactory, and enhance the clinical usefulness of the tool for dysphagia management. These results also support future studies addressing the responsiveness of the MISA.
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 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".