P4‐288: FLORTAUCIPIR DEPOSITION PATTERN ACROSS THE ALZHEIMER'S DISEASE SPECTRUM –CHARACTERIZATION AT THE INDIVIDUAL LEVEL
Bibliographic record
Abstract
The extent to which older adults follow a typical pattern of tau-PET deposition is not clear. We focused on inter-individual pattern of flortaucipir ([F]AV1451) binding across Braak stages and single brain regions in participants along the AD spectrum. We included 166 Aβ-negative (113 cognitively normal [CN], 33 eMCI, and 20 lMCI/AD) and 129 Aβ-positive (72 CN, 30 eMCI and 27 lMCI/AD) participants from ADNI with a flortaucipir PET scan. SUVRs were extracted in composite regions approximating Braak stages and in the Freesurfer Desikan atlas regions as more fine-grained measurements. In each region, we derived liberal and conservative thresholds using Gaussian-mixture models (GMM) across all participants (respectively 50% and 90% probability to be in high SUVR distribution). We then examined the distribution of elevated SUVR across all regions at the subject-level. Even when applying liberal thresholds for positivity, only 4% of Aβ-negative participants had elevated SUVR in Braak I/entorhinal and/or in further stages (Fig.1A). In Aβ-positive subjects, 17% CN, 43% EMCI and 67% LMCI/AD had elevated SUVR in Braak I (Fig.1B and Fig.2). Among these individuals, only 50% of LMCI/AD had elevated SUVRs in further stages compared to 92% in EMCI. Using smaller regions, flortaucipir spreading became evident in LMCI/AD and 8% of CN showed focal cortical binding in the absence of elevated entorhinal SUVR (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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".