Severe Impairment Rating Scale (SIRS) in assessing patients with severe dementia in Chinese Community
Bibliographic record
Abstract
Abstract Background Like other advanced countries, Hong Kong is facing the challenges of an aging population, reduced fertility, and increasing longevity. Most older people who diagnosed with moderate to severe dementia would need institutional care. Nevertheless, the current instruments available in the Chinese population are having flooring effects in measuring the functional conditions for this specific group of population. Method This study aims to validate the 14‐item Chinese Severe Impairment Rating Scale (C‐SIRS), as an instrument designed to measure functional condition of older people with moderate and severe dementia. The development of this validation study was under classical test theory to examine and test (1) the degree of clarity, understandability, and relevance (i.e. content validity), (2) the test‐retest reliability score of SIRS, (3) the degree of the inter‐relatedness among SIRS items like internal consistency, and (4) the correlation between SIRS score and the functional measures by Hong Kong Montreal Cognitive Assessment (HK‐MoCA) and Modified Barthel Index 100 (MBI‐100). Result When assessing the relevance and sensitivity of the question with the universal agreement calculation method, the scale‐level content validity index (S‐CVI/UA) of C‐SIRS received relevance ratings of “almost agree” or “absolutely agree” by all the experts. The Fleiss' kappa showed that there was moderate agreement between the panellists’ judgements, with at least 8 years of experience, κ = .326 (95% CI, .319 to .343), p < .001. 105 older people with moderate to severe dementia recruited from nursing home were enrolled in the validation, the C‐SIRS had a high level of internal consistency, as determined by a Cronbach’s alpha of 0.99 with F = .005, p = .831. The SIRS shows significant correlation with both cognitive, as measured by MoCA (with r = .337, p < .001), and functional measures as measured by MBI (with r = .64, p < .01). Conclusion C‐SIRS is sensitive in measuring the functional condition in older people with moderate to severe dementia. Moreover, C‐SIRS is easier to administrate with shorter time required. Further study should help by investigating the sensitivity of C‐SIRS to assess the change over a longer period of time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".