Early onset versus late onset in Alzheimer’s disease: What is the reliable cut-off?
Bibliographic record
Abstract
Objective: As the literature on conventional criteria for discriminating early-onset (EO) from late-onset (LO) Alzheimer’s disease (AD) is sparse and controversial, the aim of this study was to establish a precise age at onset (AAO) criterion, by using a specific statistical procedure, and to describe the clinical characteristics of the two sub-groups. Methods: Admixture analysis was performed to establish the AAO cut-off in a multi-center study including 2000 AD patients consecutively recruited in eight Italian Memory Clinics. None of the patients were taking acetylcholinesterase inhibitors, antipsychoticor anti-depressant drugs. At the first diagnosticvisit, they were administered the Mini Mental StateExamination, the Basic and Instrumental Activities of Daily Living and the Neuropsychiatric Inventorytoassess clinical phenomenology. Results: Using a specific statistical procedure, we established that AAO that discriminated EO-from LO-AD was 66. Compared with the LO-AD group, the EO-AD group showed longer duration of illness and a higher educational level as well as less severe functional impairment and delusions. Conclusions: Differences in sociodemographic and clinical characteristics, such as duration of illness, education and delusion severity, suggested the involvement of different pathogenic processes. Additional studies are needed to further investigate the mechanisms underlying the disorder in the two sub-groups of AD patients.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".