Selecting a Brief Cognitive Screening Test Based on Patient Profile: It Is Never Too Early to Start
Bibliographic record
Abstract
Introduction: Cognitive impairment, marked by a decline in memory and attention, is frequently underdiagnosed, complicating effective management. Cardiovascular risk factors (CVR) and anticholinergic burden (ACB) are significant contributors to dementia risk, with ACB often stemming from medications prescribed for neuropsychiatric disorders. This study evaluates cognitive profiles through three brief cognitive tests, analyzing the impact of CVR and ACB presence. Methods: This cross-sectional study was performed between 2019 and 2023 in community pharmacies and an outpatient clinic in Valencia, Spain. Eligible participants were patients with subjective memory complaints 50 years or older with clinical records of cardiovascular factors. Patients with conflicting information regarding diabetes diagnosis or not taking concomitant medications were excluded. Three brief cognitive tests (Memory Impairment Screening (MIS), Semantic Verbal Fluency Test, and SPMSQ) were assessed. CVR was calculated using the European SCORE2 table, and ACB was assessed using the CALS scale. Results: Among 172 patients with memory complaints and CVR factors, 60% failed at least one cognitive test. These patients were on significantly more medications and had higher blood pressure and HbA1c levels. An increase in CVR and ACB was associated with more failed tests. Additionally, elevated SCORE2 scores were associated with a greater failure rate on the MIS test, while patients with elevated ACB more frequently failed the SPMSQ test. Conclusions: Selecting an adequate brief cognitive test according to patients’ characteristics offers an opportunity to screen patients who are probably cognitively impaired. Whereas the MIS test may be helpful for patients with cardiovascular risk, SPMSQ stands out among patients with significant ACB.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".