Why has therapy development for dementia failed in the last two decades?
Bibliographic record
Abstract
The success rate of the pharmaceutical research and development (R&D) for dementia drugs has been abysmally low, in the last two decades. Also low has been the number of pipeline drugs in development, compared to other therapy areas. However, the rationale of early terminations has not been reported in the majority of trials. These are key findings of the recently published pharmaceutical pipeline analysis by the UK-based Office of Health Economics (OHE). Our understanding of main challenges include (1) the significant gaps of knowledge in the nosology and complexity of the underpinning biological mechanisms of the commonest, not familial, forms of late onset dementias; (2) low signal-to-noise ratio, notwithstanding the lack of validated biomarkers as entry and/or end-point criteria; (3) recruitment and retention, particularly in the asymptomatic and early disease stages. A number of current and future strategies aimed at ameliorating drug development are outlined and discussed.
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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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".