Early Discriminatory Diagnosis of Dementia with Lewy Bodies
Bibliographic record
Abstract
BACKGROUND: The clinical diagnostic criteria for dementia with Lewy bodies (DLB) have a low sensitivity, and there are no generally accepted biomarkers to distinguish DLB from other dementias. Our aim was to identify biomarkers that may differentiate DLB from Alzheimer's disease (AD). METHOD: We performed a systematic literature search for studies of EEG, imaging techniques and genetic and CSF markers that provide sensitivity and specificity in the identification of DLB. RESULTS: The best evidence was for scintigraphy of the striatal dopamine transporter system using FP-CIT SPECT. Several small scintigraphy studies of cardiovascular autonomic function using metaiodobenzylguanidine SPECT have reported promising results. Studies exploring innovative techniques based on CSF have reported interesting findings for the combination of amyloid beta (abeta) isoforms as well as alpha-synuclein, and there are interesting results emerging from preliminary studies applying proteomic techniques. Data from studies using structural MRI, perfusion SPECT, genetics and EEG studies show differences between DLB and AD but only at a group level. CONCLUSION: Several potential biomarkers for the differential diagnosis of probable DLB and AD have shown good diagnostic accuracy in the research setting. Data from large multicentre studies and from studies with autopsy confirmation exist for scintigraphy of the dopamine transporter system. Future studies should explore its value in possible DLB and for clinical management and health economics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".