Traumatic Encephalopathy Syndrome in the Late Effects of Traumatic Brain Injury (LETBI) study cohort
Bibliographic record
Abstract
ABSTRACT Background and Objective Traumatic encephalopathy syndrome (TES) is the proposed clinical manifestation of chronic traumatic encephalopathy (CTE) neuropathologic change secondary to repetitive head impacts (RHI). The prevalence of TES and its component symptoms is not known in individuals with single TBI, a subset of whom also have RHI. We used prospectively collected data to operationalize TES criteria and test the hypothesis that the core clinical features of TES are common among those with TBI, regardless of RHI exposure status and other demographic and injury characteristics. Methods Secondary analysis of data from the Late Effects of TBI (LETBI) study, a community-based study of individuals with complicated mild, moderate, or severe TBI. Participants were categorized by TBI severity and presence of RHI, creating 6 groups (those with isolated mild, moderate, and severe TBI, with and without RHI). Chi-squared tests were used to compare the proportion of each group that met each of the core clinical criteria overall TES diagnosis. Binary logistic regression models were used to examine associations of demographic and injury characteristics on TES diagnosis. Results In 295 participants with TBI, mean (SD) age 52.6(15.6) years and 35.6% female, 138 (46.8%) had RHI exposure meeting the TES criteria exposure threshold. In the full sample, 56.9%, 32.9% and 45.8% of participants met TES core criterion of cognitive impairment, neurobehavioral dysregulation, and progressive course of clinical features, respectively. Overall, 14.9% of the LETBI sample had substantial RHI exposure and met all 3 clinical features, meeting consensus-based TES criteria. When RHI exposure criterion was lifted, 33.5% of the LETBI sample with isolated TBI met all core clinical criteria. No injury or demographic variables predicted the likelihood of meeting TES Core Criteria (OR=3.02, p=0.10). Discussion Rates of TES clinical features are high among TBI survivors with and without RHI, across injury severity groups. Presence of TES core clinical features was greatest among those with no RHI, suggesting that chronic and sometimes progressive clinical sequelae of TBI resemble TES, but may reflect a distinct pathobiological process. Limitations include possible selection of participants with chronic symptoms. Findings emphasize the centrality of RHI exposure to TES diagnostic criteria.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".