P4‐335: LONGITUDINAL ASSESSMENT OF THE NOVEL TAU TRACER [<sup>18</sup>F]MK‐6240 FOR THE USE IN CLINICAL TRIALS
Bibliographic record
Abstract
Here, we aim to assess for the first-time longitudinal changes and the utility for clinical trials of the novel neurofibrillary tangles’ tracer [F]MK-6240. Nineteen individuals (10 cognitively unimpaired (CU) and 9 cognitively impaired (CI)) underwent PET [F]AZD4694 at baseline and [F]MK-6240 at baseline and 1-year follow-up. [F]AZD4694 and [F]MK-6240 standardized uptake value ratios (SUVRs) used the cerebellum grey matter as reference and were calculated between 40 to 70 min and 90 to 110 min, respectively. Paired t-test assessed the differences between baseline and follow-up bindings. Sample size (using multiple regions-of-interest) and voxel-wise (new method) sample size calculations were performed. All CI and 2 CU 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 clusters in the posterior cingulate, parietal, and frontal cortices (Fig.1). We did not find significant correlations between baseline [F]AZD4694 and [F]MK-6240 changes. In CU, sample size calculation revealed that a clinical trial would require as few as 260 individuals per arm to test a 25% drug effect with 80% of power at 5% level on [F]MK-6240 accumulation in the anterior cingulate cortex. Voxel-wise sample size calculation revealed that the clusters with the highest statistical power for testing disease-modifying interventions did not respect delimited anatomical boundaries and required as few as 100 individuals per arm to test the same 25% drug effect (Fig.2).
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".