Prospective Associations of Susceptibility-Weighted Imaging Biomarkers with Fatigue Symptom Severity in Childhood Traumatic Brain Injury
Bibliographic record
Abstract
Fatigue may be among the most profound and debilitating consequences of pediatric traumatic brain injury (TBI); however, neurostructural risk factors associated with post-injury fatigue remain elusive. This prospective study aimed to evaluate the independent value of susceptibility-weighted imaging (SWI) biomarkers, over-and-above known risk factors, to predict fatigue symptom severity in children with TBI. Forty-two children were examined with structural magnetic resonance imaging (sMRI), including a SWI sequence, within eight weeks post-injury. The PedsQL Multi-Dimensional Fatigue Scale (MFS) was administered 24 months post-injury. Compared with population expectations, the TBI group displayed significantly higher levels of general fatigue (Cohen d = 0.44), cognitive fatigue (Cohen d = 0.59), sleep/rest fatigue (Cohen d = 0.37), and total fatigue (Cohen d = 0.63). In multi-variate models adjusted for TBI severity, child demographic factors, and depression, we found that subacute volume of SWI lesions was independently associated with all fatigue symptom domains. The magnitude of the brain-behavior relationship varied by fatigue symptom domain, such that the strongest relationships were observed for the cognitive fatigue and total fatigue symptom scales. Overall, we found that total subacute volume of SWI lesions explained up to 24% additional variance in multi-dimensional fatigue, over-and-above known risk factors. The subacute SWI has potential to improve prediction of post-injury fatigue in children with TBI. Our preliminary findings suggest that volume of SWI lesions may represent a novel, independent biomarker of post-injury fatigue, which could help to identify high-risk children who are likely to benefit from targeted psychoeducation and/or preventive strategies to minimize risk of long-term post-injury fatigue.
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 | 0.000 | 0.003 |
| 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".