Comparison of the Quick Mild Cognitive Impairment (Qmci) screen to the Montreal Cognitive Assessment (MoCA) in an Australian geriatrics clinic
Bibliographic record
Abstract
INTRODUCTION: The Montreal Cognitive Assessment (MoCA) accurately differentiates mild cognitive impairment (MCI) from mild dementia and normal controls (NC). While the MoCA is validated in multiple clinical settings, few studies compare it with similar tests also designed to detect MCI. We sought to investigate how the shorter Quick Mild Cognitive Impairment (Qmci) screen compares with the MoCA. METHODS: Consecutive referrals presenting with cognitive complaints to a teaching hospital geriatric clinic (Fremantle, Western Australia) underwent a comprehensive assessment and were classified as MCI (n = 72) or dementia (n = 109). NC (n = 41) were a sample of convenience. The Qmci and MoCA were scored by trained geriatricians, in random order, blind to the diagnosis. RESULTS: Median Qmci scores for NC, MCI and dementia were 69 (+/-19), 52.5 (+/-12) and 36 (+/-14), respectively, compared with 27 (+/-5), 22 (+/-4) and 15 (+/-7) for the MoCA. The Qmci more accurately identified cognitive impairment (MCI or dementia), area under the curve (AUC) 0.97, than the MoCA (AUC 0.92), p = 0.04. The Qmci was non-significantly more accurate in distinguishing MCI from controls (AUC 0.91 vs 0.84, respectively = 0.16). Both instruments had similar accuracy for differentiating MCI from dementia (AUC of 0.91 vs 0.88, p = 0.35). At the optimal cut-offs, calculated from receiver operating characteristic curves, the Qmci (≤57) had a sensitivity of 91% and specificity of 93% for cognitive impairment, compared with 87% sensitivity and 80% specificity for the MoCA (≤23). CONCLUSION: While both instruments are accurate in detecting MCI, the Qmci is shorter and arguably easier to complete, suggesting that it is a useful instrument in an Australian geriatric outpatient population. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".