The Effects of Computerized Cognitive Testing of Older Patients on Primary Care Physicians’ Approaches to Care
Bibliographic record
Abstract
BACKGROUND: We evaluated effects of providing primary care physicians (PCPs) with reports of their patients' results on the Computer Assessment of Mild Cognitive Impairment (CAMCI) by examining their documented care approaches after receipt of the report. METHODS: Patients were 65 years and above, without a diagnosis or previous workup for dementia, seen consecutively over 2 months by one of 13 PCPs. PCPs indicated whether they, patients, or families had concerns about patients' cognition. A total of 259 patients individually completed the CAMCI and results were provided to the PCP. Two raters blind to CAMCI results recorded care approaches documented by PCPs at the first visit within 3 months of report (n=181). RESULTS: In total, 28 different care approaches were grouped as related to Cognition or Safety/Self-Care. Negative binomial regression revealed that the number of care approaches was significantly associated with performance on the CAMCI for both Cognition and Safety/Self-care domains. These findings remained significant when covariates included PCPs' cognitive concern before CAMCI results, and patients' age, sex, number of comorbidities, and living arrangements. CONCLUSIONS: Our findings indicate that PCPs documented more care approaches in patients with greater cognitive impairment based on the CAMCI results and this was independent of their, the patients', or families' prior concerns about their patients' cognition.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".