The value of routine screening for cognitive impairment in a primary care setting: a retrospective cross-sectional study
Bibliographic record
Abstract
Abstract BACKGROUND Cognitive impairment is not uncommon among older individuals but is often underdiagnosed in the primary care settings. Our objective is to identify the prevalence of varying degrees of cognitive impairment in older adults in urban primary care clinics. METHODS This cross-sectional study was undertaken from May to July 2019. The Montreal Cognitive Assessment (MoCA) was administered to participants 65-year and older who were seen during a routine visit to our primary care clinic. The participants were recruited on a sequential basis. The primary outcomes of the study were the MoCA scores, and the level of cognitive impairment, if any, indicated by the scores. RESULTS Out of the 133 participants, 46 (34.6%) scored below the cut-off of 23 out of 30, indicating certain level of cognitive impairment. The mean MoCA score was 23.24. The average age was 75.19 years. Average years of education was 12.6. In our cohort, higher MoCA scores were associated with increased years of education. MoCA scores were not inversely correlated with age. Language proficiency in the MoCA test version administered had significant impact on the MoCA scores. CONCLUSIONS A relatively high prevalence of cognitive impairment was found in our cohort. Further study is required to accurately assess the prevalence of cognitive impairment in general population. However, the findings attest the value of routine screening for cognitive impairment in primary care settings and warrant routine screening of older individuals to help in early detection of mild cognitive impairment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".