Recovery From Mild Head Injury in Sports: Evidence From Serial Functional Magnetic Resonance Imaging Studies in Male Athletes
Bibliographic record
Abstract
OBJECTIVE: To examine functional brain activation patterns before and after postconcussive symptoms (PCS) resolution. DESIGN: Prospective serial study with male athletes using functional magnetic resonance imaging (fMRI). SETTING: Hospital laboratory and imaging facility. PARTICIPANTS: 9 symptomatic concussed athletes who experienced persisting PCS at least 1 month postinjury and 6 healthy athletes. INTERVENTIONS: All athletes filled out a PCS checklist and underwent an fMRI session during which they performed a working-memory task. MAIN OUTCOME MEASUREMENTS: Behavioral outcomes were response speed and accuracy on the working memory tasks performed during the fMRI session. Functional imaging outcomes were blood oxygen level-dependent fMRI activation patterns associated with a working memory task. RESULTS: : There was no difference in behavioral performance between the groups. Despite normal structural MRI findings, all symptomatic concussed athletes initially showed atypical brain activation patterns in the dorsolateral prefrontal cortex (DLPC). Compared to the initial postinjury evaluation, those athletes at follow-up with PCS resolved showed significant increases in activation in the left DLPC. Concussed athletes whose PCS status remained unchanged at follow-up continued to show atypical activation in DLPC. Healthy athletes showed remarkably clear and consistent brain activations in DLPC initially as well as in follow-up, highlighting the test-retest reliability of fMRI. CONCLUSIONS: The results demonstrate the feasibility of using fMRI to detect an underlying pathology in symptomatic concussed athletes with normal structural imaging results and its potential to document recovery. Such information may be of considerable value for clinical judgment and patient management.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".