Effectiveness of Montreal Cognitive Assessment for the diagnosis of mild cognitive impairment and mild Alzheimer’s disease in Singapore
Bibliographic record
Abstract
I NTRO D U C TIO NMild cognitive impairment (MCI) is a heterogeneous clinical entity that spans between normal ageing and dementia in the elderly.(1) MCI is characterised as cognitive impairment(s) beyond normal ageing, with minimal or no decline in activities of daily living (ADL).(2) MCI represents a significant risk factor for dementia, with an annual conversion rate of about 10%-15% to mild Alzheimer's disease (AD).(3) Hence, there are important management implications surrounding this clinical entity, including cognitive and pharmacological management.(4) Early detection of MCI is crucial in order to allow optimum interventions as they become available, so as to reduce the risk of progression to dementia.Among the subtypes of MCI, amnestic MCI (MCI-A) is the most homogeneous and best characterised entity.It also has the highest rates of conversion to AD.Cognitive screening tools are useful for the clinical diagnosis of MCI.While routine cognitive tools such as the mini-mental state examination (MMSE) have been demonstrated to be effective in the detection of dementia, these tools are less effective in the detection of MCI, as most individuals with MCI score in the normal range on MMSE.(2,5) The Montreal Cognitive Assessment (MoCA) was demonstrated to have good sensitivity and specificity for the detection of MCI.(6) However, due to cultural and language differences across regions, it is likely that different MoCA cutoff points would be required to aid the diagnosis of cognitive impairment in specific countries.For instance, Lee et al in 2008 demonstrated that a cutoff of 22/23 provided the highest sensitivity when diagnosing cognitive impairment in Korea, (7) while Wen et al suggested that a cutoff of 26/27 was appropriate for a Chinese population.(8) The original study reported a cutoff point of 25/26 in a Canadian population.(6) In the present study, we aimed to determine the sensitivity and specificity of MoCA for the detection of MCI-A and mild AD in a multiracial population in Singapore.We hypothesised that a MoCA cutoff point of 26, similar to the original study by Nasreddine et al, (6) would be applicable for diagnosing MCI-A in Singapore, while a MoCA score of 24-25 would be applicable for the diagnosis of mild AD. M E TH O DSData on MCI-A and mild AD were obtained from a prospectively collected clinical database, which comprised patients with cognitive impairment managed at the National Neuroscience Institute,
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".