Evaluation of Dementia: The Case for Neuroimaging All Mild to Moderate Cases
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to assess the usefulness of 4 clinical prediction rules, the neuroimaging guidelines from the Canadian Consensus Conference on Dementia (CCCAD) and the modified Hachinski's Ischaemic Score (HIS) in identifying patients with suspected dementia who will benefit from neuroimaging. MATERIALS AND METHODS: Two hundred and ten consecutive patients were referred to the memory clinic in a geriatric unit for the evaluation of possible dementia. Sensitivity, specificity and likelihood ratios (LR) were calculated for each of the prediction rules and the CCCAD guidelines, in terms of their ability to identify patients with significant lesions [defined firstly as space-occupying lesions (SOL) alone and secondly as SOL or strokes] on neuroimaging. Similar analyses were applied for the HIS in the detection of strokes. RESULTS: When considering SOL alone, sensitivities ranged from 28.6% to 100% and specificities ranged from 21.7% to 88.4%. However, when strokes were included in the definition of significant lesions, sensitivities ranged from 16.2% to 79.0% and specificities ranged from 20.9% to 92.4%. The modified HIS had a similarly poor sensitivity and specificity (43.3% and 78.9% respectively). The LR for the clinical decision tools did not support the use of any particular instrument. CONCLUSIONS: Clinical decision tools do not give satisfactory guidance for determining the need for neuroimaging patients with suspected dementia, when the detection of strokes, in addition to SOL, is regarded as important. We recommend therefore that neuroimaging be considered for all patients with suspected mild or moderate dementia in whom the potential benefits of any treatment outweigh the potential risks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".