Clinical judgment of GPs for the diagnosis of dementia: a diagnostic test accuracy study
Bibliographic record
Abstract
BACKGROUND: GPs often report using clinical judgment to diagnose dementia. AIM: To investigate the accuracy of GPs' clinical judgment for the diagnosis of dementia. DESIGN & SETTING: Diagnostic test accuracy study, recruiting from 21 practices around Bristol, UK. METHOD: The clinical judgment of the treating GP (index test) was based on the information immediately available at their initial consultation with a person aged ≥70 years who had cognitive symptoms. The reference standard was an assessment by a specialist clinician, based on a standardised clinical examination and made according to the 10th revision of the International Classification of Diseases (ICD-10) criteria for dementia. RESULTS: A total of 240 people were recruited, with a median age of 80 years (interquartile range [IQR] 75-84 years), of whom 126 (53%) were men and 132 (55%) had dementia. The median duration of symptoms was 24 months (IQR 12-36 months) and the median Addenbrooke's Cognitive Examination III (ACE-III) score was 75 (IQR 65-87). GP clinical judgment had sensitivity 56% (95% confidence interval [CI] = 47% to 65%) and specificity 89% (95% CI = 81% to 94%). Positive likelihood ratio was higher in people aged 70-79 years (6.5, 95% CI = 2.9 to 15) compared with people aged ≥80 years (3.6, 95% CI = 1.7 to 7.6), and in women (10.4, 95% CI = 3.4 to 31.7) compared with men (3.2, 95% CI = 1.7 to 6.2), whereas the negative likelihood ratio was similar in all groups. CONCLUSION: A GP clinical judgment of dementia is specific, but confirmatory testing is needed to exclude dementia in symptomatic people whom GPs judge as not having dementia.
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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 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".