Further validation of the Danish version of the McGill Ingestive Skills Assessment (MISA-DK): Rasch analysis
Bibliographic record
Abstract
Background/aims The McGill Ingestive Skill Assessment (MISA) for measuring dysphagic patients' functional performance during meals has been previously translated into Danish — the Danish McGill Ingestive Skill Assessment (MISA-DK) and this translated version validated. However, issues about the construct validity of the scale had been raised. Therefore, this study aims to further validate and revise the MISA-DK using Rasch analysis. Methods An extended Rasch analysis was performed on data from a former MISA-DK validation study, which resulted in revisions to the scale. The revised MISA-DK was then tested using 102 videorecordings of geriatric patients' ingestive skill performance, and the data from the scale were examined using a second Rasch analysis. Results Initially, two of the six proposed subscales of the original MISA-DK failed to fit the Rasch model, and were removed. It was also necessary to revise one item due to differential item functioning and to develop additional items as its reliability was too low. A second Rasch analysis of the revised MISA-DK with four subscales showed fit to the Rasch model after adjusting local item dependence throughout the scale. Conclusion A revised MISA-DK demonstrated satisfactory construct validity when measured by the Rasch model. As local item dependence appeared to be a feature of the scale, the application of more complex Rasch models is warranted for further validation.
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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 | 0.052 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".