Factors Associated With Primary Care Physicians’ Recognition of Cognitive Impairment in Their Older Patients
Bibliographic record
Abstract
Although there are accurate screens for cognitive impairment, there is as yet no evidence that screening improves outcomes including primary care physicians' (PCP) medical decision making. PCPs' recognition of cognitive impairment being suboptimal, we investigated factors associated with improved recognition. Eligible patients were aged 65 years and above, without documented dementia or previous work-up for dementia, seen consecutively over 2 months by one of 13 PCPs. PCPs indicated whether they, the patient, or the family had concerns about each patient's cognition. We enrolled 130 patients with any cognitive concerns and a matched sample of 133 without cognitive concerns, and administered standardized neuropsychological tests. PCP's judgments of cognitive concern showed 61% sensitivity and 86% specificity against the neuropsychological standard. When combined with a Mini-Mental State Examination score ≤26, PCP recognition improved in sensitivity (82%) with some loss in specificity (74%). True positives increased when PCPs' practices included more cognitively impaired patients and when patients reported poor memory. False positives increased when patients had diabetes, reported poor memory, or no or light alcohol consumption. Medical decision making can be improved by the Mini-Mental State Examination and greater exposure to cognitively impaired patients, but knowledge of certain risk factors for cognitive impairment negatively affected these decisions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".