Using subjective cognitive decline to identify high global amyloid in community‐based samples: A cross‐cohort study
Bibliographic record
Abstract
Abstract Introduction We aimed to examine the contribution of subjective cognitive decline (SCD) to reduce the number of β‐amyloid (Aβ) positron emission tomography scans required for recruiting Aβ+ clinically normal individuals in clinical trials. Methods Three independent cohorts (890 clinically normal: 72 yrs ± 6.7; Female: 43.4%; SCD+: 24%; apolipoprotein E [APOE] ε4+: 28.5%; Aβ+: 32%) were used. SCD was dichotomized from one question. Using logistic regression, we classified Aβ+ using the SCD dichotomy, APOEε4, sex, and age. Results SCD increased odds of Aβ+ by 1.58 relative to non‐SCD. Female APOEε4 carriers with SCD exhibited higher odds of Aβ+ (OR = 3.34), whereas male carriers with SCD showed a weaker, opposing effect (OR = 0.37). SCD endorsement reduces the number of Aβ positron emission tomography scans to recruit Aβ+ individuals by 13% and by 9% if APOEε4 status is known. Conclusion SCD helps to classify those with high Aβ, even beyond the substantial effect of APOE genotype. Collecting SCD is a feasible method for targeting recruitment for those likely on the AD trajectory.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".