Chronic traumatic encephalopathy: How serious a sports problem is it?
Bibliographic record
Abstract
It is now recognised that there is a spectrum of concussion disorders ranging from acute concussion at one end to various forms of brain degeneration at the other end. The spectrum includes acute concussion, second impact syndrome or acute cerebral swelling, postconcussion syndrome, depression or anxiety, chronic traumatic encephalopathy (CTE) and possibly other forms of central nervous system degeneration. It is essential to carefully evaluate the clinical and neuropathological correlations of CTE that have been published. This has been accomplished in an excellent paper on this subject by Gardner and colleagues in this issue. There have been significant advances in our knowledge of the clinical and neuropathological features of CTE in athletes in the past 10 years. However, we are just at the beginning of our appreciation of this entity due to the paucity of research and the inability to diagnose CTE during life. At present, it is not possible to assess the validity of the proposed methods of classification and grading of the severity of the disease. Additional studies of large numbers of at-risk athletes are essential, especially prospective longitudinal studies. Obviously, such studies would be even more effective if reliable in vivo biomarkers were discovered, especially non-invasive ones such as advanced MRI or MR spectroscopy or invasive ones such as blood or cerebrospinal fluid tests. The major questions that remain unanswered include the frequency of CTE in various collision sports, the causal or otherwise relationship between concussions and CTE, the number of concussions that need to be involved and their management.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".