Increased spatial extent of cerebral tau PET elevations in former NFL and college football players from the DIAGNOSE CTE Research Project
Bibliographic record
Abstract
Abstract Background We previously found greater spatial extent of flortaucipir (tau) PET elevations in Arizona‐Boston (DETECT) Study of 26 former National Football League (NFL) players than in 31 normal controls—using a voxel‐based Majority Count Statistics (MCS) algorithm which enabled us to detect tau PET abnormalities in the player group free from the inflated Type 1 error associated with voxel‐wise multiple comparisons (Stern et al, NEJM 2019). We now confirm this finding in a larger group of former NFL players, former college football players and asymptomatic controls without exposure to repetitive head impacts from the DIAGNOSE CTE Research Project. Method Flortaucipir PET images were acquired in 106 former NFL players, 51 former college players, and 53 unexposed controls, 57.71+8.37 years of age. An automated algorithm (SPM12) and co‐registered MRIs were used to spatially normalize PET images into Montreal Neurological Institute template, and compute statistical brain maps of group differences in regional cerebral‐to‐cerebellar crus1 standard uptake value ratio (SUVR), controlling for the participants’ age and race with P≤0.005, uncorrected for multiple comparisons. Our MCS was then applied with 1,000 iterations to test the hypothesis that there would be a greater number of cerebral voxels with SUVR elevations in each and combined player group versus control group, as well as between the NFL versus college player groups. Result As predicted, the NFL, college player and aggregate player groups each had significantly more cerebral voxels with flortaucipir SUVR elevations than in the control group (postulated vs opposite directions: 53,879 vs 79, 79,552 vs 89 and 100,837 vs 86 voxels), respectively, MCS P<0.001, but not between former NFL and former college players (482 versus 306, p = 0.305). Conclusion Using a voxel based MCS analysis, former NFL and college football players have a greater spatial extent of elevations in tau PET measurements than asymptomatic controls. Additional work is needed to clarify the biological nature of these small but spatially extensive elevations, the extent to which they are related to symptom severity, cognitive performance, repetitive head impact exposure, and other risk or resilience factors, and the extent to which they are associated with subsequent clinical decline.
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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 | 0.002 | 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 | 0.000 |
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".