MétaCan
Menu
← Back to cohort
Record W4390199416 · doi:10.1002/alz.077856

White matter hyperintensities: A long‐term consequence of repetitive head impacts?

2023· article· en· W4390199416 on OpenAlexaff
Michael L. Alosco, Monica T. Ly, Fatima Tuz‐Zahra, Yorghos Tripodis, Charles H. Adler, Laura J. Balcer, Charles Bernick, Elaine R. Peskind, Megan Mariani, Rhoda Au, Sarah J. Banks, William Barr, Jennifer V. Wethe, Mark W. Bondi, Lisa Delano‐Wood, Robert C. Cantu, Michael J. Coleman, David W. Dodick, Michael D. McClean, Jesse Mez, Joseph Palmisano, Brett Martin, Alexander Lin, Inga K. Koerte, Jeffrey L. Cummings, Eric M. Reiman, Martha E. Shenton, Robert A. Stern, Sylvain Bouix

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHyperintensityMedicineFluid-attenuated inversion recoveryAmerican footballInternal medicinePsychologyFootballCardiologyPhysical medicine and rehabilitationMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Background Repetitive head impacts (RHI) from American football can lead to tau and non‐tau pathologies that might present as white matter hyperintensities (WMH) on FLAIR MRI. In 2022, we published a study in Alzheimer’s & Dementia that examined WMH and their association with risk factors and clinical function in former elite football players. That study will be presented during this symposium along with new unpublished data that used imaging and fluid biomarkers to examine etiological correlates (tau, amyloid, neurodegeneration, axonal injury, neuroinflammation) of WMH in the same cohort. Methods The multi‐site DIAGNOSE CTE Research Project recruited 180 football players and 60 asymptomatic males without RHI (“controls”), all 45‐74 years (Table 1). Participants completed MRI, lumbar puncture, and neuropsychological testing. Lesion Segmentation Toolbox estimated FLAIR WMH, FreeSurfer derived total cortical thickness, and a diffusion pipeline derived average global FA. CSF was analyzed for p‐tau181, Aβ1‐42, NfL, and sTREM2. Tobit regressions compared football players (n = 149) and controls (n = 53) on total and regional log‐WMH and estimated the effects of years of football and age of first exposure (AFE) to football on log‐WMH. Linear regressions evaluated log‐WMH and clinical associations in football players. Structural equation modeling (SEM) examined effects between log‐WMH and cortical thickness, FA, and the CSF biomarkers. Analyses accounted for age, race, revised Framingham Stroke Risk Profile (rFSRP), body mass index, APOE ε4, and evaluation site. Results Alosco et al. (2022) found older (i.e., 60+) but not younger football players had greater total, frontal, temporal and parietal log‐WMH compared to controls (FDR‐adjusted p‐values<0.05; Table 2). Among older football players, younger AFE was associated with greater log‐WMH (beta = ‐0.13, 95% CI = ‐0.23,‐0.02). Greater log‐WMH corresponded to worse Trails A‐B (beta = ‐4.32, 95% CI = ‐8.59,‐0.05) and List Learning Long Delay scores (beta = ‐0.56, 95% CI = ‐0.93,‐0.19). Subsequent unpublished SEM analyses in football players (Figure 1) showed the following direct effects on log‐WMH: higher rFSRP, higher CSF p‐tau181, lower FA, and reduced cortical thickness. There were no indirect effects. Compared to controls, SEM associations were stronger in football players exception for cortical thickness. Conclusions FLAIR WMH might have unique imaging characteristics, risk factors, and pathological underpinnings in people exposed to RHI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.091
GPT teacher head0.364
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicTraumatic Brain Injury Research→French-language works237,207→