Longitudinal changes in brain metabolites following pediatric concussion
Bibliographic record
Abstract
Concussion is commonly characterized by a cascade of neurometabolic changes following injury. Magnetic Resonance Spectroscopy (MRS) can be used to quantify neurometabolites non-invasively. Longitudinal changes in neurometabolites have rarely been studied in pediatric concussion, and fewer studies consider symptoms. This study examines longitudinal changes of neurometabolites in pediatric concussion and associations between neurometabolites and symptom burden. Participants who presented with concussion or orthopedic injury (OI, comparison group) were recruited. The first timepoint for MRS data collection was at a mean of 12 days post-injury (n = 545). Participants were then randomized to 3 (n = 243) or 6 (n = 215) months for MRS follow-up. Parents completed symptom questionnaires to quantify somatic and cognitive symptoms at multiple timepoints following injury. There were no significant changes in neurometabolites over time in the concussion group and neurometabolite trajectories did not differ between asymptomatic concussion, symptomatic concussion, and OI groups. Cross-sectionally, Choline was significantly lower in those with persistent somatic symptoms compared to OI controls at 3 months post-injury. Lower Choline was also significantly associated with higher somatic symptoms. Although overall neurometabolites do not change over time, choline differences that appear at 3 months and is related to somatic symptoms.
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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 | 0.001 |
| 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".