A systematic review of potential long-term effects of sport-related concussion
Bibliographic record
Abstract
OBJECTIVE: Systematic review of possible long-term effects of sports-related concussion in retired athletes. DATA SOURCES: Ten electronic databases. STUDY SELECTION: 10 years after the injury. DATA EXTRACTION: Study population, exposure/outcome measures, clinical data, neurological examination findings, cognitive assessment, neuroimaging findings and neuropathology results. Risk of bias and level of evidence were evaluated by two authors. RESULTS: Following review of 3819 studies, 47 met inclusion criteria. Some former athletes have depression and cognitive deficits later in life, and there is an association between these deficits and multiple prior concussions. Former athletes are not at increased risk for death by suicide (two studies). Former high school American football players do not appear to be at increased risk for later life neurodegenerative diseases (two studies). Some retired professional American football players may be at increased risk for diminishment in cognitive functioning or mild cognitive impairment (several studies), and neurodegenerative diseases (one study). Neuroimaging studies show modest evidence of macrostructural, microstructural, functional and neurochemical changes in some athletes. CONCLUSION: Multiple concussions appear to be a risk factor for cognitive impairment and mental health problems in some individuals. More research is needed to better understand the prevalence of chronic traumatic encephalopathy and other neurological conditions and diseases, and the extent to which they are related to concussions and/or repetitive neurotrauma sustained in sports.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".