IC‐02‐02: LONGITUDINAL EVALUATION OF TAU PROPAGATION USING [<sup>18</sup>F]MK‐6240
Bibliographic record
Abstract
Here, we evaluated longitudinal changes in tau pathology in space and density using [F]MK-6240. Twenty-eight elderly individuals (15 cognitive unimpaired (CU) and 13 cognitively impaired (CI)) underwent [F]MK-6240 PET at baseline and 1-year. Changes in SUVR uptake and paired t-test assessed differences in ligand magnitude between baseline and follow-up. Alternatively, we performed a tau propagation analysis. First, we determined threshold of abnormality at every voxel based on 9 young individuals (mean age=24.5y.o). We then identified the voxels that became abnormal at follow-up visit for each individual. Finally, we created a parametric map showing the percentage of individuals that became abnormal in each brain voxel. All CI and 7 CU individuals were amyloid positive. In CI, there was no significant increase in [F]MK-6240 uptake over 1 year. In CU, we found a significant increase in [F]MK-6240 uptake in the anterior cingulate, medial prefrontal, and hippocampal cortices. The highest averaged magnitude of change was found in anterior cingulate (∼7%) and prefrontal cortex. On the other hand, propagation analysis revealed that regions that became abnormal at follow-up were confined to the hippocampus and entorhinal cortex, reaching 40% of individuals in large clusters (Fig.1).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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 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".