Comparison of Outcomes Among Neurovascular Patients Managed in Dedicated Neurological Intensive Care Units vs. General Intensive Care Units
Bibliographic record
Abstract
Background/Objectives: Patients with neurovascular conditions often require multidisciplinary management to optimize recovery. Our systematic review identifies literature comparing outcomes among neurovascular patients managed at dedicated neurological intensive care units (ICUs) compared to general ICUs. Methods: PubMed was searched to identify articles that reported outcomes among patients managed at dedicated neurological ICUs versus general ICUs. Articles that reported outcomes among patients with neurovascular conditions were included. Articles that reported outcomes among patients managed at stroke units were excluded. The Newcastle Ottawa Scale (NOS) was used to assess for risk of bias across individual studies. Results: After a title and abstract screen followed by a full-text review, seven studies met criteria for inclusion. These studies reported outcomes among patients managed for intracerebral hemorrhage (ICH), acute ischemic stroke (AIS) and aneurysmal subarachnoid hemorrhage (aSAH). Two studies reported lower mortality, improved functional outcome and reduced costs among patients with ICH who were managed at dedicated neurological ICUs. Among patients with aSAH, only less-severe cases experienced better functional outcome after management at dedicated neurological ICUs. Six out of seven studies were considered high quality. Conclusions: Our review highlights the potential benefits of receiving care at dedicated neurological ICUs, as evidenced by lower mortality, improved functional outcome and reduced costs in patients with ICH and low-grade aSAH. However, future research is necessary to clarify whether dedicated neurological ICU care confers significant advantage over general ICUs among patients with AIS and other neurovascular conditions.
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".