Neuroscience Intermediate-Level Care Units Staffed by Intensivists: Clinical Outcomes and Cost Analysis
Bibliographic record
Abstract
INTRODUCTION:: With an aging population and increasing numbers of intensive care unit admissions, novel ways of providing quality care at reduced cost are required. Closed neurointensive care units improve outcomes for patients with critical neurological conditions, including decreased mortality and length of stay (LOS). Small studies have demonstrated the safety of intermediate-level units for selected patient populations. However, few studies analyze both cost and safety outcomes of these units. This retrospective study assessed clinical and cost-related outcomes in an intermediate-level neurosciences acute care unit (NACU) before and after the addition of an intensivist to the unit's care team. METHODS:: Starting in October 2011, an intensivist-led model was adopted in a 16-bed NACU unit, including daytime coverage by a dedicated intensivist. Data were obtained from all patients admitted 1 year prior to and 2 years after this intervention. Primary outcomes were LOS and hospital costs. Safety outcomes included mortality and readmissions. Descriptive and analytic statistics were calculated. Individual and total patient costs were calculated based on per-day NACU and ward cost estimates and significance measured using bootstrapping. RESULTS:: A total of 2931 patients were included over the study period. Patients were on average 59.5 years and 53% male. The most common reasons for admission were central nervous system (CNS) tumor (27.6%), ischemic stroke (27%), and subarachnoid hemorrhage (11%). Following the introduction of an intensivist, there was a significant reduction in NACU and hospital LOS, by 1 day and 3 days, respectively. There were no differences in readmissions or mortality. Adding an intensivist produced an individual cost savings of US$963 in NACU and US$2687 per patient total hospital stay. CONCLUSION:: An intensivist-led model of intermediate-level neurointensive care staffed by intensivists is safe, decreases LOS, and produces cost savings in a system increasingly strained to provide quality neurocritical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".