Non-neurological organ dysfunction in neurocritical care: impact on outcome and etiological considerations
Bibliographic record
Abstract
PURPOSE OF REVIEW: Organ dysfunction is an important determinant of outcome in critical care medicine. Patients with life threatening neurologic injury represent a distinct subset of critically ill patients in whom non-neurologic organ dysfunction may develop. In this paper the incidence and impact of non-neurologic organ dysfunction in patients with major neurologic injury will be reviewed. Further, potential etiological considerations will be addressed and management strategies discussed. RECENT FINDINGS: Non-neurologic organ dysfunction is extremely common in patients with brain injury occurring in 80-90% of patients admitted to intensive-care units. Several studies have now identified this dysfunction as an independent predictor of poor outcome in neurocritical care. This dysfunction may arise as a result of the neurologic injury or secondary to treatment. Massive catecholamine release continues to be the primary etiological theory of non-neurologic organ dysfunction due to brain injury. Currently employed therapies directed at intracranial hypertension such as maintenance of cerebral perfusion pressure and the use of hypothermia or barbiturates predispose non-neurologic organ dysfunction. SUMMARY: Non-neurologic organ dysfunction is common. This dysfunction independently predicts poor outcome following brain injury and represents a potentially modifiable risk factor. Further study is required to develop optimal management strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".