Navigating the future of Alzheimer’s care in Ireland - a service model for disease-modifying therapies in small and medium-sized healthcare systems
Bibliographic record
Abstract
BACKGROUND: A new class of antibody-based drug therapy with the potential for disease modification is now available for Alzheimer's disease (AD). However, the complexity of drug eligibility, administration, cost, and safety of such disease modifying therapies (DMTs) necessitates adopting new treatment and care pathways. A working group was convened in Ireland to consider the implications of, and health system readiness for, DMTs for AD, and to describe a service model for the detection, diagnosis, and management of early AD in the Irish context, providing a template for similar small-medium sized healthcare systems. METHODS: A series of facilitated workshops with a multidisciplinary working group, including Patient and Public Involvement (PPI) members, were undertaken. This informed a series of recommendations for the implementation of new DMTs using an evidence-based conceptual framework for health system readiness based on [1] material resources and structures and [2] human and institutional relationships, values, and norms. RESULTS: We describe a hub-and-spoke model, which utilises the existing dementia care ecosystem as outlined in Ireland's Model of Care for Dementia, with Regional Specialist Memory Services (RSMS) acting as central hubs and Memory Assessment and Support Services (MASS) functioning as spokes for less central areas. We provide criteria for DMT referral, eligibility, administration, and ongoing monitoring. CONCLUSIONS: Healthcare systems worldwide are acknowledging the need for advanced clinical pathways for AD, driven by better diagnostics and the emergence of DMTs. Despite facing significant challenges in integrating DMTs into existing care models, the potential for overcoming challenges exists through increased funding, resources, and the development of a structured national treatment network, as proposed in Ireland's Model of Care for Dementia. This approach offers a replicable blueprint for other healthcare systems with similar scale and complexity.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".