Multiple Pathologies are Common in Alzheimer Patients in Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of multiple pathology [Alzheimer Disease (AD) plus Vascular Dementia and/or Dementia with Lewy Bodies] in patients enrolled in clinical trials of AD therapy, and to compare the cognitive and functional assessments between patients with pure AD and AD with multiple pathology. METHODS: We conducted a retrospective analysis of patients with a clinical diagnosis of AD who were enrolled in AD therapy clinical trials and subsequently received an autopsy for confirmation of their diagnosis from 2000 to 2009. Performance on cognitive screening tests, namely Modified Mini Mental state (3MS) exam, Mini Mental state Exam (MMSE) and Functional Rating Scale (FRS) were compared between patients with pure AD and multiple pathology. RESULTS: Autopsy reports were available for 16/47 (34%) of deceased patients. Of these 16 patients, 5 (31%) had pure AD pathology, 10 (63%) had AD with other pathology, and 1 (6%) had non-AD pathology. Compared to patients with pure AD, patients with AD mixed with other pathology had poorer baseline FRS in problem-solving (p<0.01) and community affairs (p<0.02). CONCLUSION: While the strict enrollment criteria for clinical trials identified the presence of AD pathology in the majority of cases (15/16), multiple pathology was more common than pure AD in our series of autopsied patients. Premortem biomarkers that can distinguish between pure AD and AD with multiple pathology will be beneficial in future clinical trials and dementia patient management.
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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 | 0.041 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".