Sex differences in off-target binding using tau positron emission tomography
Bibliographic record
Abstract
Off-target binding in the skull and meninges is observed in some subjects undergoing tau positron emission tomography (PET) and could potentially differ between men and women. In this study we elucidate sex differences in tau off-target binding using three different tau PET tracers. 541 cognitively unimpaired amyloid-β negative participants underwent tau PET using [18F]flortaucipir (n = 165), [18F]RO948 (n = 189) and [18F]MK6240 (n = 187). Baseline SUVR-values were compared between females and males at the voxel level and using a region-of-interest (ROI) encompassing the skull/meninges. In addition, we assessed the cross-sectional relationship between baseline skull/meninges SUVR and age and assessed change in skull/meningeal SUVR values over time in a subsample with longitudinal data (n = 63). Voxel-wise analysis showed higher meningeal off-target binding in women compared to men across all three tracers. The SUVRs in the skull/meningeal ROI were highest using [18F]RO948, followed by [18F]MK6240 and [18F]flortaucipir (p < 0.001). For all tracers, females showed higher skull/meningeal ROI retention (mean SUVR ± SD [18F]flortaucipir: 0.82 ± 0.14; [18F]RO948: 1.26 ± 0.30; [18F]MK6240: 1.09 ± 0.19) compared to men ([18F]flortaucipir: 0.70 ± 0.11; [18F]RO948: 1.10 ± 0.24; [18F]MK6240: 0.97 ± 0.17) (p < 0.001). For [18F]flortaucipir and [18F]RO948, off-target binding in the skull/meninges decreased with age. There is an effect of sex on off-target retention in the meninges/skull across [18F]flortaucipir, [18F]RO948, and [18F]MK6240 tau PET tracers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".