Normative scores of the Cambridge Cognitive Examination-Revised in healthy Spanish population
Bibliographic record
Abstract
BACKGROUND: The Cambridge Cognitive Examination-Revised (CAMCOG) is widely used in clinical, epidemiological and research studies, but normative scores for age and educational level have not yet been established in the Spanish population. METHOD: The CAMCOG-R was administered to 730 adult members ofthe community, aged between 50-97 years, living throughout the region of Galicia. Initial screening yielded provisional identification of cognitive impairment and depressive symptoms. The final sample consisted of 643 cognitively healthy adults. The following instruments were administered: a questionnaire concerning socio-demographic and clinical data, the Charlson's Comorbidity Index, the Mini-Mental State Examination, the Montreal Cognitive Assessment (MoCA), the Lawton and Brody Index, a short version of the Geriatric Depression Scale, and the CASP-19 quality of life scale. RESULTS: Internal consistency values of the CAMCOG-R were similar to those obtained for the original scale. The convergent validity between MoCA and CAMCOG-R was good, and the divergent validity between CASP-19 and CAMCOG-R was higher than the recommended value. Percentiles and inter-quartile range for age and educational level were calculated. CONCLUSIONS: Psychometric indexes showed that the CAMCOG-R is a reliable and valid instrument, which can generally avoid a ceiling effect. The study findings confirm the importance of specifying the normative data by age and educational level.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".