Contributions de la neuro-imagerie à l'étude des commotions cérébrales reliées au sport
Bibliographic record
Abstract
Sports-related concussions affect between 1,6 and 3 millions athletes every year in the United States alone and are a major public health concern. This injury is typically characterized as mild and transient, resolving in a few days (10-12 in adults while children and adolescents typically recover in 3-4 weeks). However, recent research demonstrates that sports-related concussions are much more severe than was previously thought and repeated concussions could increase an athlete's vulnerability to a neurodegenerative disease such as mild cognitive impairment (MCI) (a condition that converts at a rate of about 10-20% annually into dementia of Alzheimer's type) and early onset Alzheimer's disease compared to the general population. To compound the difficulty of understanding concussive injuries, conventional neuroimaging such as CT scans and MRI do not reveal any gross structural changes in the vast majority of cases. Given this alarming epidemiologic data, the severity of the sequelae, and the clinical limitations of conventional imaging, research using different advanced neuroimaging techniques to investigate the acute and long-term effects of sports-related concussions has exploded in the last two decades. The goal of these studies has been to better understand the structural and functional alterations that occur as a result of sports-related concussions. As such, the aim of the current article is to review the scientific literature that employed neuroimaging techniques such as electroencephalography (EEG), evoked-response potentials (ERP), functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), transcranial magnetic stimulation (TMS), and diffusion tensor imaging (DTI). Although future studies are still required, these results provide a better understanding of the pathophysiological mechanisms underpinning the symptomatology of sports-related concussions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".