Validating a Pragmatic Approach to Cognitive Screening in Stroke Prevention Clinics Using the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Montreal Cognitive Assessment (MoCA) is used commonly to identify cognitive impairment (CI), but there are multiple published cut points for normal and abnormal. We seek to validate a pragmatic approach to screening for moderate-severe CI, by classifying patients into high-, intermediate-, and low-risk categories. METHODS: A total of 390 participants attending an academic Stroke Prevention Clinic completed the MoCA and more detailed neuropsychological testing. Between April 23, 2012 and April 30, 2014, all consecutive new referrals to the regional Stroke Prevention Clinic who were English-speaking, not severely aphasic, and could see and write well enough to complete neuropsychological testing were assessed for inclusion, and consenting patients were enrolled. CI was defined as ≥2 SDs below normal for age and education on at least 2 cognitive subtests. A single cut point for CI was compared with 2 cut points (high sensitivity and high specificity) generated using receiver operator characteristic and area under the curve analyses. The intermediate-risk group contained those scoring between the 2 cut points. RESULTS: Thirty-four percent of participants had a symptomatic or silent stroke, 34% were seen for possible or probable transient ischemic attack, and 32% were diagnosed with other vascular or nonvascular conditions. Using a single cut point, sensitivity and specificity were optimal with MoCA ≤22, (sensitivity=60.4%, specificity=89.9%, area under the curve=0.801, positive predictive value=48.5%, negative predictive value=93.5%, positive likelihood ratio=6, and negative likelihood ratio=0.4). Using 2 cut points, sensitivity was optimal with MoCA ≥28 (sensitivity=96.2%, negative predictive value =97.6%, and negative likelihood ratio=1.27), and specificity was optimal with MoCA ≤22 (specificity=89.9%, positive predictive value=48.5%, and positive likelihood ratio=6). CONCLUSIONS: Stratifying participants into 3 categories facilitates the identification of a homogenous group at low risk for CI, as well as 2 other groups with intermediate and higher risk. This approach could facilitate clinical care pathways and patient selection for research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.141 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".