The ENIGMA Sports Injury Working Group: an International Collaboration to Further our Understanding of Sports-Related Brain Injury
Bibliographic record
Abstract
Sports-related brain injury is very common, and the potential long-term effects include a widerange of neurological and psychiatric symptoms, and potentially neurodegeneration. Aroundthe globe, researchers are conducting neuroimaging studies on primarily homogenoussamples of athletes. However, neuroimaging studies are expensive and time consuming, andthus current findings from studies of sports-related brain injury are often limited by smallsample size. Further, current studies apply a variety of neuroimaging techniques and analysistools which limit comparability among studies. The ENIGMA Sports Injury working group aimsto provide a platform for data sharing and collaborative data analysis thereby leveragingexisting data and expertise. By harmonizing data from a large number of studies from aroundthe globe, we will work towards reproducibility of previously published findings and towardsaddressing important research questions with regard to diagnosis, prognosis, and efficacy oftreatment for sport-related brain injury. Moreover, the ENIGMA Sports Injury working group iscommitted to providing recommendations for future prospective data acquisition to enhancefurther, both, data quality and scientific rigor.
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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 | 0.076 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.026 |
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".