Determining the impact of psychosis on rates of false‐positive and false‐negative diagnosis in Alzheimer's disease
Bibliographic record
Abstract
Abstract Introduction The rate of clinical misdiagnosis of Alzheimer's disease (AD) and how psychosis impacts that clinical judgment is unclear. Methods Using data from National Alzheimer's Coordinating Center, we compared the clinical and neuropathologic diagnosis in patients with a diagnosis of AD with autopsy and in neuropathology‐confirmed AD cases ( n = 961). We determined the rate of true positives, false positives, and false negatives in patients with and without psychosis. Results A total of 76% received a correct AD diagnosis, 11.9% had a false‐negative diagnosis, and 12.1% had a false‐positive diagnosis of AD. Psychotic patients had a higher rate of false‐negative diagnosis and a lower rate of false‐positive diagnosis of AD compared with nonpsychotic patients. Discussion Patients with psychosis were five times more likely to be misdiagnosed as dementia with Lewy bodies, whereas patients without psychosis were more likely to be falsely diagnosed with AD when vascular pathology is the underlying neuropathologic cause of dementia.
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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 | 0.059 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".