Concussion Acutely Decreases Plasma Glycerophospholipids in Adolescent Male Athletes
Bibliographic record
Abstract
Concussions are frequent in sports and can contribute to significant and long-lasting neurological disability. Adolescents are particularly susceptible to concussions, with accurate determination of the injury challenging. Our previous study demonstrated that concussion diagnoses could be aided by metabolomics profiling and machine learning, with particular weighting on changes in plasma glycerophospholipids (PCs). Here, our aim was to report directional change of PCs after concussion and develop a diagnostic concussion panel utilizing a minimum number of plasma PCs. To this end, we enrolled 12 concussed male athletes at our academic Sport Medicine Concussion Clinic, as well as 17 sex-, age-, and activity-matched healthy controls. Blood was drawn and 71 plasma PCs were measured for statistically significant changes within 72 h of injury, and individual PCs were further analyzed with receiver operating characteristic (ROC) curves. Our data demonstrated that 26 of 71 PCs measured were significantly decreased after sports-related concussion (p < 0.01). None of the PCs increased in plasma after concussion. ROC curve analyses identified the top four PCs with areas under the curve (AUCs) ≥0.86 for concussion diagnosis: PCaeC36:0 (0.92; p < 0.001); PCaaC42:6 (0.90; p < 0.001); PCaeC36:2 (0.86; p = 0.001), and PCaaC32:0 (0.86; p = 0.001). Cut-off values in μM were ≤0.31, 0.22, 5.07, and 4.63, respectively. Importantly, combining these four PCs produced an AUC of 0.96 for concussion diagnoses (p < 0.001; 95% confidence interval, 0.89, 1.00). Our data suggest that as few as four circulating PCs may provide excellent diagnostic potential for adolescent concussion. External validation is required in larger cohorts.
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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 | 0.000 | 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".