Prevalence and clinicopathological features of primary age‐related tauopathy (PART): A large forensic autopsy study
Bibliographic record
Abstract
INTRODUCTION: Primary age-related tauopathy (PART), often regarded as a minimally symptomatic pathology of old age, lacks comprehensive cohorts across various age groups. METHODS: We examined PART prevalence and clinicopathologic features in 1589 forensic autopsy cases (≥40 years old, mean age ± SD 70.2 ± 14.2 years). RESULTS: PART cases meeting criteria for argyrophilic grain diseases (AGD) were AGD+PART (n = 181). The remaining PART cases (n = 719, 45.2%) were classified as comorbid conditions (PART-C, n = 90) or no comorbid conditions (pure PART, n = 629). Compared to controls (n = 208), Alzheimer's disease (n = 133), and AGD+PART, PART prevalence peaked in the individuals in their 60s (65.5%) and declined in the 80s (21.5%). No significant clinical background differences were found (excluding controls). However, PART-C in patients inclusive of age 80 had a higher suicide rate than pure PART (p < 0.05), and AGD+PART showed more dementia (p < 0.01) and suicide (p < 0.05) than pure PART. DISCUSSION: Our results advocate a reevaluation of the PART concept and its diagnostic criteria. HIGHLIGHTS: We investigated 1589 forensic autopsy cases to investigate the features of primary age-related tauopathy (PART). PART peaked in people in their 60s in our study. Many PART cases over 80s had comorbid pathologies in addition to neurofibrillary tangles pathology. Argyrophilic grain disease and Lewy pathology significantly affected dementia and suicide rates in PART. Our results suggest that the diagnostic criteria of PART need to be reconsidered.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".