Comparing Diagnostic Timelines And Referral Pathways In Alzheimer’s Disease And Lewy Body Dementia: Insights From A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background Timely diagnosis is essential for effective dementia care, yet Lewy Body Dementia (LBD) may lead to delays in diagnosis compared to Alzheimer’s Disease (AD). LBD accounts for over 20% of the 65,000 people living with dementia in Ireland. Despite this prevalence, fewer than 5% of individuals with LBD receive a diagnosis, leading to delayed care and support. Compared to AD, LBD diagnosis often involves longer timelines and greater healthcare resources. This study compared the diagnostic and post-diagnostic pathways of patients with AD and LBD, examining referral patterns, diagnostic timelines, and follow-up interventions. Methods A retrospective cohort study was conducted using electronic medical records of patients assessed in 2023 at the Memory and Movement Disorder Clinics. Data were extracted for 40 patients diagnosed with Alzheimer’s Disease (AD) and 40 with Lewy Body Dementia (LBD), including Dementia with Lewy Bodies (DLB) and Parkinson’s Disease Dementia (PDD). Information collected included reason for referral, time to diagnosis, and healthcare professionals involved in initial assessment and diagnosis. Details of post-diagnostic assessments and interventions were also recorded. Descriptive and comparative analyses were conducted to evaluate differences in diagnostic timelines and referral pathways between AD and LBD patients. Results The findings revealed significant differences in diagnostic timelines and referral pathways between patients with AD and LBD. LBD patients experienced more delays, misdiagnoses, and were more often referred for second opinions compared to AD patients. Furthermore, LBD patients required more healthcare resources before and after diagnosis, including additional diagnostic tests, joint specialist consultations, and multidisciplinary assessments and interventions across various healthcare services. Conclusion The findings of this study indicate that the diagnostic and post-diagnostic pathways for LBD patients are more complex and resource-demanding than AD patients. These results highlight the need for a streamlined, standardised, and integrated care pathway to ensure timely and effective diagnosis and management of LBD.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".