Health economic evaluation of treatments for Alzheimer′s disease: impact of new diagnostic criteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it