Adaptation of the Montreal Cognitive Assessment for elderly Filipino patients.
Bibliographic record
Abstract
OBJECTIVE: The Montreal Cognitive Assessment (MoCA) is an instrument that aids clinicians in detecting mild cognitive impairment and early Alzheimer's disease. The study aimed to adapt the MoCA for use in the Philippines, and determine its psychometric validity when used in the Filipino setting. METHODS: The MoCA was adapted by a multidisciplinary team working at the Memory Center of St. Luke's Medical Center, Manila, the Philippines. Contextual adaptation, rather than direct translation, was done. Pilot testing of the Filipino version of the MoCA (MoCA-P) was done on 12 grade 6 pupils and subsequently on 14 cognitively intact elderly people. Reliability testing of the MoCA-P was done on 25 elderly people by trained psychologists. Internal consistency, inter-rater and intra-rater reliability, as well as convergent and divergent validity of the MoCA-P were determined. RESULTS: The MoCA-P yielded a high level of internal consistency (Cronbach's alpha; = 0.938). Inter-rater and intra-rater reliability were 0.887 (p ≤ 0.05) and 0.969 (p ≤ 0.05), respectively. The MoCA-P correlated negatively with the Epworth Sleepiness Scale (r = -0.233) and had a positive but low correlation with the Mini-Mental State Examination (r = 0.555). CONCLUSION: Contextual translation and pilot testing yielded several modifications of the MoCA. The MoCA-P is a reliable instrument for use in elderly Filipino patients. Further diagnostic validation of the MoCA-P to establish cutoff scores that would discriminate elderly individuals with normal cognition from those with dementia is needed to establish the clinical utility of the test.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".