Chronic traumatic encephalopathy (CTE) is absent from a European community-based aging cohort while cortical aging-related tau astrogliopathy (ARTAG) is highly prevalent
Bibliographic record
Abstract
Chronic traumatic encephalopathy (CTE) and aging-related tau astrogliopathy (ARTAG) are characterised by tau-immunopositive neuronal and/or astrocytic inclusions, with overlapping cortical involvement and astrocytic inclusion morphology. This study determined the prevalence of CTE and cortical ARTAG in a European community-based population (n=310) and explored overlap of both pathological entities. Frontal, parietal and temporal cortices were assessed. No case fulfilling CTE criteria was found. However, isolated astroglial or neuronal tau pathologies were recognized in sulcal depths (<2%). One case without history of traumatic brain injury showed combined tau-immunoreactive features confined to frontal sulci without perivascular accumulation. Another 24 cases had single tau pathologies in cortical sulci. ARTAG was identified in 117 cases (38%), with a similar regional prevalence. Grey matter ARTAG was the most common followed by subpial, white matter and perivascular. The presence of any type of ARTAG was associated with having another type of ARTAG in the same region (P<0.05). In summary, cortical ARTAG in this population is common and contrasts the high prevalence of CTE in individuals with repeated mild traumatic brain injury. LEARNING OBJECTIVES This presentation will enable the learner to: Classify tau-immunopositive astrocytic inclusions characteristic of ARTAG 1. Describe neuropathological components of CTE 2. Identify CTE and cortical ARTAG in a case series
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".