A Diagnostic Test Accuracy Study Investigating General Practitioner Clinical Impression and Brief Cognitive Assessments for Dementia in Primary Care, Compared to Specialized Assessment
Bibliographic record
Abstract
BACKGROUND: Many health systems are interested in increasing the number of uncomplicated and typical dementia diagnoses that are made in primary care, but the comparative accuracy of tests is unknown. OBJECTIVE: Calculate diagnostic accuracy of brief cognitive tests in primary care. METHODS: We did a diagnostic test accuracy study in general practice, in people over 70 years who had consulted their GP with cognitive symptoms but had no prior diagnosis of dementia. The reference standard was specialist assessment, adjudicated for difficult cases, according to ICD-10. We assessed 16 index tests at a research clinic, and additionally analyzed referring GPs clinical judgement. RESULTS: 240 participants had a median age of 80 years, of whom 126 were men and 132 had dementia. Sensitivity of individual tests at the recommended thresholds ranged from 56% for GP judgement (specificity 89%) to 100% for MoCA (specificity 16%). Specificity of individual tests ranged from 4% for Sniffin' sticks (sensitivity 100%) to 91% for Timed Up and Go (sensitivity 23%). The 95% centile of test duration in people with dementia ranged from 3 minutes for 6CIT and Time and Change, to 16 minutes for MoCA. Combining tests with GP judgement increased test specificity and decreased sensitivity: e.g., MoCA with GP Judgement had specificity 87% and sensitivity 55%. CONCLUSIONS: Using GP judgement to inform selection of tests was an efficient strategy. Using IQCODE in people who GPs judge as having dementia and 6CIT in people who GPs judge as having no dementia, would be a time-efficient and accurate diagnostic assessment.The original protocol for the study is available at https://bmcfampract.biomedcentral.com/articles/10.1186/s12875-016-0475-2.
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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 | 0.015 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".