The International Concussion and Head Injury Research Foundation Brain health in Retired athletes Study of Ageing and Impact-Related Neurodegenerative Disease (ICHIRF-BRAIN Study)
Bibliographic record
Abstract
Abstract Introduction and aims Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Large registry studies have demonstrated a dose–response relationship between TBI and neurodegenerative disease ; however, disentangling the direct effects of TBI from ageing and/or a progressive neurodegenerative process is problematic. This study is a prospective long-term cohort study to examine a population of retired elite athletes at high risk of concussion and mTBI during their sporting careers compared to age- and sex-matched controls with no history of TBI. The aim is to determine the incidence and risk factors for neurodegenerative disease and/or age-related effects on brain health in this population. Methods and analysis A population of retired male and female elite athletes and controls aged 40-85 years, will be assessed at baseline and serial time points over 10 years during life using a multi-dimensional assessment including: Questionnaire; SCAT3/5; Neurological and physical examination; Instrumented balance assessment; Computerised neurocognitive screen; Neuropsychological assessment; Advanced MR brain neuroimaging; Visual saccades; Blood workup; Fluid biomarkers; Gut metabolomics; Salivary MicroRNA analysis; Genetic analysis; and where available Brain banking and neuropathology Ethics and dissemination Ethics approval was granted by St Mary’s University SMEC as well as at the various satellite trial sites. The trial is registered with ISRCTN (BioMed Central) with ID number: 11312093. In addition to the usual dissemination process, this phenotypically well-characterised dataset will reside in a publicly accessible infrastructure of integrated databases, imaging repositories, and biosample repositories and de-identified data will be made available to collaborating researchers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".