Intracranial and Extracranial Injury Burden as Drivers of Impaired Cerebrovascular Reactivity in Traumatic Brain Injury
Bibliographic record
Abstract
Impaired cerebrovascular reactivity has been associated with outcome following traumatic brain injury (TBI), but it is unknown how it is affected by trauma severity. Thus, we aimed to explore the relationship between intracranial (IC) and extracranial (EC) injury burden and cerebrovascular reactivity in TBI patients. We retrospectively included critically ill TBI patients. IC injury burden included detailed lesion and computerized tomography (CT) scoring (i.e., Marshall, Rotterdam, Helsinki, and Stockholm Scores) on admission. EC injury burden was characterized using the injury severity score (ISS) and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. Pressure reactivity index (PRx), pulse amplitude index (PAx), and RAC were used to assess autoregulation/cerebrovascular reactivity. We used univariate and multi-variate logistic regression techniques to explore relationships between IC and EC injury burden and autoregulation indices. A total of 358 patients were assessed. ISS and all IC CT scoring systems were poor predictors of impaired cerebrovascular reactivity. Only subdural hematomas and thickness of subarachnoid hemorrhage (SAH; p < 0.05, respectively) were consistently associated with dysfunctional cerebrovascular reactivity. High age (p < 0.01 for all) and admission APACHE II scores (p < 0.05 for all) were the two variables most strongly associated with abnormal cerebrovascular reactivity. In summary, diffuse IC injury markers (thickness of SAH and the presence of a subdural hematoma) and APACHE II were most associated with dysfunction in cerebrovascular reactivity after TBI. Standard CT scoring systems and evidence of macroscopic parenchymal damage are poor predictors, implicating potentially both microscopic injury patterns and host response as drivers of dysfunctional cerebrovascular reactivity. Age remains a major variable associated with cerebrovascular reactivity.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".