The impact of informant-related characteristics including sex/gender on assessment of Alzheimer's disease symptoms and severity
Bibliographic record
Abstract
Alzheimer's disease (AD) is a debilitating neurological disorder affecting millions of people worldwide. Early and accurate diagnosis of AD is crucial for accessing treatments (including clinical trials), planning for the future, and obtaining necessary services. Unfortunately, AD and other neurodegenerative diseases associated with cognitive impairment or behavioral changes, are frequently misdiagnosed or diagnosed at later stages. Experts recommend a multifaceted approach that integrates performance, informant, and self-report data to properly assess patients with neurodegenerative diseases (1). This approach allows clinicians to assess the presence and severity of cognitive disorders, establish a differential diagnosis, and formulate an effective treatment plan. Caregivers play a pivotal role in recognizing early signs of the disease, such as memory loss and behavioral changes. They provide valuable insights to healthcare providers, assisting in diagnosis and treatment (2). In contrast to conditions where self-reporting is standard, caregiver input is instrumental in AD assessments, especially in clinical trials where disease severity is a critical criterion and endpoint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".