Serum Biomarkers Help Predict Attention Problems in Critically Ill Children With Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between acute serum biomarkers, and the changes in attention at 1 year following traumatic brain injury. DESIGN AND SETTING: A prospective observational and laboratory study conducted in PICUs at five Canadian children's hospitals. STUDY POPULATION AND MEASUREMENTS: Fifty-eight patients aged 5 to 17 years with traumatic brain injury were enrolled in the study. Nine brain-specific and inflammatory serum protein biomarkers were measured multiple times over the first week following injury. Attention was measured at "baseline" to represent pre-injury function and at 1 year following injury using the Conners Third Parent Rating Scale. RESULTS: Compared with baseline, there were significantly more clinical symptoms of inattention at 1 year post injury. The Glasgow Coma Scale score, age at injury, baseline levels of inattention, and highest levels of serum biomarkers were used to estimate the probability of developing inattention. These independent variables were first evaluated individually followed by combinations of the best predictors using area under the receiver operating characteristic curve analyses. A combination of high baseline levels of inattention and high serum levels of the biomarker neuron-specific enolase was the best predictor for inattention. Glasgow Coma Scale and age at injury were not associated with inattention at 1 year post injury. CONCLUSIONS: Combining baseline assessment of attention with measurement of serum biomarkers shows promise as reliable, early predictors of long-term attention after childhood traumatic brain injury.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".