The Montreal Cognitive Assessment After Omission of Hearing‐Dependent Subtests: Psychometrics and Clinical Recommendations
Bibliographic record
Abstract
OBJECTIVES: Hearing loss (HL) is the third most common chronic health condition in older adults, yet it is often undiagnosed and/or untreated. Given the association between HL and cognitive impairment, it is expected that many people undergoing cognitive screening may have HL. The Montreal Cognitive Assessment (MoCA) is a brief screening test that assesses a wide range of cognitive functions sensitive to Alzheimer's disease (AD) and mild cognitive impairment (MCI). Although MoCA items were carefully designed to be sensitive to deficits in MCI, they were not designed to take sensory declines into account. In the current investigation, we examined the MoCA's psychometric properties following omission of subtests primarily dependent on hearing status (memory, digit span, attention to letters, and sentence repetition). DESIGN: Cross-sectional analytic design (retrospective analysis). SETTING: PARTICIPANTS: Groups consisted of healthy controls (N = 90), subjects with MCI (N = 94), and subjects with mild AD (N = 93). MEASUREMENTS: We assessed sensitivity and specificity using absolute and proportional cutoff score adjustments. We developed receiver operating characteristics curves to determine the best cutoff values for both MCI and AD patients using different combinations of auditory subtest omissions. RESULTS: Compared with the original MoCA (MCI sensitivity = 90%; specificity = 87%), MCI sensitivity was substantially reduced (absolute scoring = 43%; proportional scoring = 56%) when all auditory subtests were omitted, with the biggest contribution to the reduction coming from the delayed recall subtest. Excluding three subtests and maintaining the delayed recall had no effect on MCI sensitivity but reduced specificity (sensitivity = 94%, specificity: 71% using proportional scoring). AD sensitivity, in contrast, was not strongly influenced by our manipulation and remained relatively high through all three subtest omission combinations. CONCLUSION: The current study highlights the contribution of hearing-dependent subtests on the sensitivity and specificity of the MoCA. Clinical recommendations related to these findings are discussed. J Am Geriatr Soc 67:1689-1694, 2019.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".