Initial Diagnosis and Management of Acutely Elevated Intracranial Pressure
Bibliographic record
Abstract
Acutely elevated intracranial pressure (ICP) may have devastating effects on patient mortality and neurologic outcomes, yet its initial detection remains difficult because of the variety of manifestations that it can cause disease states it is associated with. Several treatment guidelines exist for specific disease processes such as trauma or ischemic stroke, but their recommendations may not apply to other causes. In the acute setting, management decisions must often be made before the underlying cause is known. In this review, we present an organized, evidence-based approach to the recognition and management of patients with suspected or confirmed elevated ICP in the first minutes to hours of resuscitation. We explore the utility of invasive and noninvasive methods of diagnosis, including history, physical examination, imaging, and ICP monitors. We synthesize various guidelines and expert recommendations and identify core management principles including noninvasive maneuvers, neuroprotective intubation and ventilation strategies, and pharmacologic therapies such as ketamine, lidocaine, corticosteroids, and the hyperosmolar agents mannitol and hypertonic saline. Although an in-depth discussion of the definitive management of each etiology is beyond the scope of this review, our goal is to provide an empirical approach to these time-sensitive, critical presentations in their initial stages.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".