Health economic evaluation of treatments for Alzheimer′s disease: impact of new diagnostic criteria
Bibliographic record
Abstract
The socio-economic impact of Alzheimer's disease (AD) and other dementias is enormous, and the potential economic challenges ahead are clear given the projected future numbers of individuals with these conditions. Because of the high prevalence and cost of dementia, it is very important to assess any intervention from a cost-effectiveness viewpoint. The diagnostic criteria for preclinical AD suggested by the National Institute on Aging and Alzheimer's Association workgroups in combination with the goal of effective disease-modifying treatment (DMT) are, however, a challenge for clinical practice and for the design of clinical trials. Key issues for future cost-effectiveness studies include the following: (i) the consequences for patients if diagnosis is shifted from AD-dementia to predementia states, (ii) bridging the gap between clinical trial populations and patients treated in clinical practice, (iii) translation of clinical trial end-points into measures that are meaningful to patients and policymakers/payers and (iv) how to measure long-term effects. To improve cost-effectiveness studies, long-term population-based data on disease progression, costs and outcomes in clinical practice are needed not only in dementia but also in predementia states. Reliable surrogate end-points in clinical trials that are sensitive to detect effects even in predementia states are also essential as well as robust and validated modelling methods from predementia states that also take into account comorbidities and age. Finally, the ethical consequences of early diagnosis should be considered.
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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 | 0.059 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".