Clinical Utility of the Mini-Mental Status Examination When Assessing Decision-Making Capacity
Bibliographic record
Abstract
The main objectives of this study were to examine the relationship between cognitive deficits, as measured by the Mini-Mental Status Examination (MMSE), and decision-making capacity and to determine whether the sensitivity and specificity of the MMSE varied based upon the patient population assessed. Using a sample size of 152 patients and varying cutoff scores, the MMSE demonstrated extremely poor sensitivity. In contrast, the MMSE had excellent specificity when scores of 19 or less were obtained. In our sample, not one patient, regardless of diagnosis, was deemed to have capacity if their MMSE score was below 20. However, reliance on the MMSE for scores above 19 would too frequently lead to misclassification and incorrect assumptions about a patient's decision-making abilities. Although a score below 20 consistently yielded findings of incapability in our sample, it remains our opinion that the MMSE should not be used as a stand-alone tool to make determinations related to capacity, especially when considering the complexities associated with capacity evaluations and the vital areas, such as executive functioning and individual values and beliefs, which are omitted by the MMSE.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".