Recommendations on Imaging in the Context of Alzheimer’s Disease-Modifying Therapies from the CCNA Imaging Workgroup
Bibliographic record
Abstract
BACKGROUND: Disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are emerging following successful clinical trials of therapies targeting amyloid beta (Aβ) protofibrils or plaques. Determining patient eligibility and monitoring treatment efficacy and adverse events, such as Aβ-related imaging abnormalities, necessitates imaging with MRI and PET. The Canadian Consortium on Neurodegeneration in Aging (CCNA) Imaging Workgroup aimed to synthesize evidence and provide recommendations on implementing imaging protocols for AD DMTs in Canada. METHODS: The workgroup employed a Delphi process to develop these recommendations. Experts from radiology, neurology, biomedical engineering, nuclear medicine, MRI and medical physics were recruited. Surveys and meetings were conducted to achieve consensus on key issues, including protocol standardization, scanner strength, monitoring protocols based on risk profiles and optimal protocol lengths. Draft recommendations were refined through multiple iterations and expert discussions. RESULTS: The recommendations emphasize standardized acquisition imaging protocols across manufacturers and scanner strengths to ensure consistency and reliability of clinical treatment decisions, tailored monitoring protocols based on DMTs' safety and efficacy profiles, consistent monitoring regardless of perceived treatment efficacy and MRI screening on 1.5T or 3T scanners with adapted protocols. An optimal protocol length of 20-30 minutes was deemed feasible; specific sequences are suggested. CONCLUSION: The guidelines aim to enhance imaging data quality and consistency, facilitating better clinical decision-making and improving patient outcomes. Further research is needed to refine these protocols and address evolving challenges with new DMTs. It is recognized that administrative, financial and logistical capacity to deliver additional MRI and positron emission tomography scans require careful planning.
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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 | 0.096 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.025 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".