Development and initial validation of the Cognitive Change Scale (CCS)
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is common in neurologic diseases. Precise measurement of cognitive change over time is necessary for isolating disease-related patterns from normal age-related decline. Existing measures of subjective cognition, however, focus on present status. There is, to our knowledge, no currently available self-report measure of cognitive change. We therefore developed the Cognitive Change Scale (CCS), which assesses perceived cognitive change in neurologic populations. METHODS: A systematic mixed-methods process was followed for the scale design and validation. Associations of CCS responses to demographics, mood, and fatigue were examined in 131 persons with multiple sclerosis. A total of 46 participants also completed a cognitive test battery. Correlations of test scores with CCS responses were calculated. RESULTS: The 17-item CCS showed good reliability and validity. Results of exploratory and confirmatory factor analyses supported a four-factor structure, with items reflecting change in (1) general cognition, (2) language and executive function, (3) external feedback, and (4) use of coping strategies. Positive relationships of CCS scores with fatigue, depression, and anxiety were observed. Correlations of CCS scores with cognitive test performance did not reach significance. CONCLUSION: The CCS may be a useful cognitive outcome tool for treatment trials in neurologic populations.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".