The Operational and Economic Impact of a Neurovascular Unit in an Acute Care Academic Hospital
Bibliographic record
Abstract
BACKGROUND: There is strong evidence that clinical outcomes are improved for stroke patients admitted to specialized Stroke Units. The Toronto Western Hospital (TWH) created a Neurovascular Unit (NVU) using resources from General Internal Medicine, Neurology, and Neurosurgery for patients with stroke and acute neurovascular conditions. Under resource-constrained conditions, the operational and economic impacts of the Neurovascular Unit were unknown. METHODS: Retrospective patient-level data was studied from two years prior and one year post NVU implementation. Descriptive statistical analysis and non-parametric testing were conducted on the acute length of stay (LOS), alternate level of care LOS, total cost per bed-day and per visit, and patient flow within each medical service and hospital wide. RESULTS: The median acute LOS per hospitalization for NVU-eligible patients decreased significantly (p=0.001). For Neurology patients, mean acute LOS decreased from 9.1 days pre-Neurovascular Unit to 7.6 days post and median acute LOS decreased from 6 to 5 days (p=0.002); however, mean alternate level of care LOS per visit more than doubled (from 1.6 to 4.1 days, p=0.001). For the Neurology service, the mean cost per visit decreased by $945, representing a 5% reduction (p=0.042) and the mean cost per bed-day decreased by $233, or 12.5% (p=0.026). Hospital wide, a saving of over C$450 000 was achieved. CONCLUSIONS: During the first year of operation, the NVU at TWH achieved decreased acute LOS per visit and lowered the total hospitalization cost per year for NVU-eligible patients. Addressing the issue of increased alternate level of care LOS could result in additional efficiencies.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".