Validation of the Sinhala version of the Montreal Cognitive Assessment in screening for dementia
Bibliographic record
Abstract
OBJECTIVES: To validate the Sinhala version of the Montreal Cognitive Assessment (MoCA) scale in screening for dementia. METHODS: The MoCA translation and cultural adaptation was carried using a combined qualitative and quantitative approach. Sample size was calculated to detect a targeted sensitivity of 85% and a specificity of 85%. Sample consisted of 49 participants diagnosed with dementia of the Alzheimer's type (AD) according to DSM-IV criteria and 49 normal controls (NC) aged ≥50 years. All subjects were administered the Mini Mental State Examination (MMSE) and MoCA Sinhala version (MoCA-S). Concurrent validity was assessed using Pearson correlation coefficients between the MoCA-S scores and MMSE scores. Criterion validity was assessed using receiver operating characteristic (ROC) analysis. RESULTS: Mean MoCA scores between NC (26.71, SD 2.4) and AD group (16.78, SD 5.9) were significantly different (t=10.8, p<0.001). Cronbach's alpha of 0.818 indicated good internal consistency. Attention (digit span, sustained attention, and the serial 7 calculation task) had the highest discriminant ability followed by visuospatial skills (trail making, cube drawing and clock drawing). Naming had poor discriminant ability. There was a high, positive correlation between MoCA-S total scores and MMSE total scores. (r=0.907, p<0.001). The area under the ROC curve was 0.975 (95%CI 0.94-1.0) for the MoCA and 0.928 (95% CI 0.87-0.98) for MMSE. A cut-off value of 24 provided the best balance between sensitivity (98.0 7%) and specificity (79.6 %). CONCLUSION: MoCA-S is a valid and reliable instrument which can be used as a brief screening instrument for dementia in Sri Lanka.
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 | 0.011 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".