Clinicopathological concordance of dementia diagnoses by community versus tertiary care clinicians
Bibliographic record
Abstract
Subjects enrolled in the Autopsy Program at the University of Southern California Alzheimer's Disease Research Center may receive clinical diagnoses from primary care providers in the community or from specialists in neurology. We reviewed the autopsy concordance rates for 463 subjects for diagnoses made by both groups of clinicians. Seventy-seven percent of the sample met neuropathological criteria for Alzheimer's disease (AD). The overall diagnostic accuracy for this sample was 81 percent. Neurologists assessed 200 of the subjects (43 percent). The diagnostic accuracy for any clinical diagnosis among the non-neurologists was 84 percent, and 78 percent (p = 0.07) among neurologists. For AD, non-neurologists had a diagnostic concordance rate of 91 percent and neurologists 87 percent. Where neuropathological AD was missed, non-neurologists had failed to detect any cognitive impairment; neurologists had diagnosed Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Erroneous clinical diagnoses of AD missed dementia with Lewy bodies (DLB) or AD concurrent with Parkinson's disease (PD). Our findings identify specific foci for improving clinical diagnosis of dementia among all physicians managing 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".