A National Dementia Registry for Ireland; A Feasibility Analysis
Bibliographic record
Abstract
Background: There is an acknowledged urgent need to gather valid epidemiological data in Ireland that provides accurate and reliable estimates of current and future dementia prevalence and facilitates the development of effective dementia health and social policy. This study, commissioned by the Alzheimer Society of Ireland, examined the feasibility of developing a National Dementia Registry for Ireland. \nMethods: National and international patient registry literature was reviewed to identify registry functions, models, best-practice guidelines, and the legal, ethical, clinical, technology, and financial issues relevant to the creation of a dementia registry in Ireland. Following ethical approval, we conducted two focus groups with people with dementia and twenty-one expert interviews with representatives from Irish and UK research, health, and social care organisations, Irish patient registries and international dementia registries. Discussions followed an agreed structure, were audio-recorded, transcribed and analysed using inductive content analysis. \nResults: Six themes emerged from these analyses: registry function; registry data; data collection; data management; registry governance and legislation. Three cross-cutting superordinate themes were also identified: benefits and risks, barriers and facilitators, and dementia-specific challenges. \nConclusion: These findings provide an evidence-base from which we draw key conclusions and recommend actions to develop a comprehensive National Dementia Registry for Ireland.
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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 | 0.205 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".