Do Mini–Mental State Examination and Montreal Cognitive Assessment predict high‐cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing
Bibliographic record
Abstract
OBJECTIVES: Policymakers want to better identify in advance the 10% of people who account for approximately 75% of health care costs. We evaluated how well Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) predicted high costs in Ireland. METHODS/DESIGN: We used five waves from The Irish Longitudinal Study on Ageing, a biennial population-representative survey of people aged 50+ (2010-2018). We used competing risks analysis where our outcome of interest was "high costs" (top 10% at any wave) and the competing outcome was dying or loss to follow-up without first having the high-cost outcome. Our binary predictors of interest were a 'low score' (bottom 10% in the sample) in MMSE (≤25 pts) and MoCA (≤19 pts) at baseline, and we calculated sub-hazard ratios after controlling for sociodemographic, clinical and functional factors. RESULTS: Of 5856 participants, 1427 (24%) had the 'high cost' outcome; 1463 (25%) had a competing outcome; and 2966 (51%) completed eight years of follow-up without either outcome. In multivariable regressions a low MoCA score was associated with high costs (SHR: 1.38 (95% CI: 1.2-1.6) but a low MMSE score was not. Low MoCA score at baseline had a higher true positive rate (40%) than did low MMSE score (35%). The scores had similar association with exit from the study. CONCLUSIONS: MoCA had superior predictive accuracy for high costs than MMSE but the two scores identify somewhat different types of high-cost user. Combining the approaches may improve efforts to identify in advance high-cost users.
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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".