MoCA cutoff score in relation to the functional assessment of seniors living in a rural Canadian community.
Bibliographic record
Abstract
INTRODUCTION: Research suggests that the Montreal Cognitive Assessment (MoCA) normal cutoff score of 26 may not be appropriate for all populations and ages. We sought to determine an appropriate MoCA cutoff score for community-dwelling seniors living in a rural Canadian community. METHODS: We conducted a retrospective chart review at a health centre in rural northern Ontario. The sample included community-dwelling seniors presenting between Dec. 1, 2013, and July 31, 2015. We generated a receiver operating characteristic curve to evaluate MoCA cutoff scores in relation to functional assessment, using the dichotomous categories of "no deficiencies in activities of daily living/instrumental activities of daily living (ADL/IADL) and "deficiencies in ADL/IADL." RESULTS: A total of 95 charts were included in the chart review. We identified MoCA scores of 20 (sensitivity 85%, specificity 62%) and 21 (sensitivity 77%, specificity 77%) as cutoff scores for the identification of impairment in this rural population. CONCLUSION: Our results suggest the normal range in MoCA score for the community-dwelling rural senior to be between 22 and 30. Although the MoCA demonstrated satisfactory performance as a screening measure, the importance of including ADL and IADL functional assessments before making clinical decisions cannot be overemphasized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".