Validity and psychometric properties of the Severe Cognitive Rating Scale‐English version, among individuals with dementia
Bibliographic record
Abstract
Abstract Background Commonly used screening measures of cognitive function such as the Montreal Cognitive Assessment (MoCA) are not sensitive to assess cognitive function among individuals with severe cognitive impairment due to floor effect. The Severe Cognitive Impairment Rating Scale (SCIRS) was designed to assess cognitive function in those with severe cognitive impairment, however, psychometric properties of its English version have not been reported. Method Using the existing data from StaN and tTED studies, floor and ceiling effects (percentage of minimal or maximal scores) of SCIRS and MoCA were examined, and the association between SCIRS and MoCA was evaluated. Result Data from 141 participants (mean age = 78.7, 56% females) who completed either the SCIRS (n = 122) or MoCA (n = 80) were collected (n = 61 completed both). There was robust association between SCIRS and MoCA, supporting criterion validity of the SCIRS as a measure of cognitive function. SCIRS had a lower floor effect (13.1% minimal scores) as compared to the MoCA (27.5% minimal scores). Out of 22 participants with minimal scores on the MoCA, 16 participants completed the SCIRS with mean score of 9.8 (SD = 7.5). Conclusion SCIRS appears to be a valid measure of cognitive function, showing better variance among individuals with severe cognitive impairment, as compared to MoCA.
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 | 0.006 | 0.018 |
| 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 | 0.001 | 0.001 |
| 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".