Refining MoCA Interpretation in An Outpatient Clinic Via Logistic Regression and Base Rate Adjustment: A Pilot Study
Bibliographic record
Abstract
Objectives The Montreal Cognitive Assessment (MoCA) is a screening measure for mild cognitive impairment (MCI) and major neurocognitive disorder (MNCD). Typical cutoff scores for the MoCA are ≤ 26 for MCI and ≤ 18 for MNCD; however; overreliance on cutoff scores can result in misdiagnosis. Our study used logistic regression analyses and predictive probabilities at a range of base rates to view MoCA scores along a continuum rather than a dichotomous variable. Methods Data from 49 memory clinic outpatients were analyzed. Logistic regressions and base rate adjustments were used to provide predicted probabilities of no diagnosis, MCI, and MNCD for possible scores on the MoCA at different base rates. Results Using cumulative logistic regression, 11 cases were predicted to have no diagnosis (22.4%), 25 to have MCI (51%), and 13 to have MNCD (26.5%). Using standardized beta coefficients, scores of ≥24, 18-23, and ≤17 resulted in predictive probabilities of .50 or greater when diagnosing normal cognition (NC), MCI, and MNCD, respectively. Finally, predictive probabilities were calculated for MoCA scores at diagnosis base rates ranging from 10% to 90%. Conclusions Cutoff scores commonly used to assist with interpreting MoCA results may limit its diagnostic utility due to treating cognitive impairment as a dichotomous variable. Our study highlights how logistic regression analyses can be used to refine MoCA interpretation by viewing scores along a continuum and accounting for base rates of a condition of interest within a population, thus providing more meaningful test findings for providers and patients and their caregivers.
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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 | 0.036 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".