Bibliographic record
Abstract
The article by Martins et al.1 in this issue of Neurology proposes a novel approach to study the rate of progression in Alzheimer disease (AD). Predicting the course of AD is important for counseling patients and caregivers and for planning studies of intervention. Accurate modeling of the course of AD may also provide insights into its pathophysiology. The common APOE e4 allele confers an increased risk in late-onset AD.2 APOE e4 may be associated with beta-amyloid and tangle pathology.3 The less common APOE e2 allele may reduce risk. The rate of progression after the onset of AD is not as clearly related to the APOE allele, but if the APOE allele is associated with progression of pathology, earlier disease onset should be associated with more rapid progression. The current inconsistent relationship between APOE allele and prognosis has been difficult to reconcile with its relationship to onset of disease. Modeling the clinical course of AD relies on appropriate study designs: following a well-defined sample of patients; enrolling patients at the same stage of disease; and …
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".