Bibliographic record
Abstract
What Was the Question? How can health policy decision-makers prepare for the potential future use of new and emerging treatments for Alzheimer disease (AD) and other dementias? What Did We Do? We identified emerging technologies for early diagnosis. We assessed the infrastructural capacity to deliver amyloid-targeted therapy in Canada, including the availability of PET-CT imaging equipment for confirming treatment eligibility, access to MRI units for monitoring treatment side effects, and IV infusion clinics for administering the treatment. We engaged with clinicians who treat people with dementia, researchers involved with dementia-related health research, and people with dementia and their caregivers. What Did We Find? There are several emerging diagnostic technologies — including blood, imaging, saliva, and ocular tests, and artificial intelligence algorithms — that could diagnose AD in its early stages more easily and quickly. The availability of PET-CT and MRI units, radiopharmaceuticals, and cyclotrons is currently not sufficient to accommodate the implementation of amyloid-targeted therapies in Canada. People living with dementia described barriers to accessing adequate, appropriate, and equitable care. Clinicians and researchers said that access to timely and reliable diagnosis must be improved. What Does This Mean? Our work highlights recent advances in AD diagnosis and treatment, related health system gaps in terms of accessing diagnostic testing and treatment, and the unmet needs of people living with dementia and their caregivers. It is important that health systems prepare for the potential surge in the number of people with dementia who might need additional diagnostic tests, treatments, monitoring, and models of care.
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.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".