Rates, Characteristics, and Outcomes of Patients Transferred to Specialized Stroke Centers for Advanced Care
Bibliographic record
Abstract
Background While many patients are transferred to specialized stroke centers for advanced acute ischemic stroke (AIS) care, few studies have characterized these patients. We sought to determine variation in the rates and differences in the baseline characteristics and clinical outcomes between AIS cases presenting directly to stroke centers' front door versus Transfer-Ins from another hospital. Methods and Results We analyzed 970 390 AIS cases in the Get With The Guidelines-Stroke registry from January 2010 to March 2014 to compare hospitals with high Transfer-In rates (≥15%) versus those with low Transfer-In rates (<5%) and to compare the front-door versus Transfer-In patients admitted to those hospitals with high Transfer-In rates (high Transfer-In hospitals). Of 970 390 patients discharged from 1646 hospitals, 87% initially presented via the emergency department versus 13% were a Transfer-In from another hospital. High Transfer-In hospitals had a median 31% Transfer-In rate among all stroke discharges, were larger, had higher annual AIS volume and intravenous tPA (tissue-type plasminogen activator) rates, and were more often Midwest teaching hospitals and stroke centers. Compared with front-door, Transfer-In patients were younger, more often white, had higher median National Institutes of Health Stroke Scale scores, less often hypertension and previous stroke/transient ischemic attack, and higher in-hospital mortality (7.9% versus 4.9%; standardized difference, 12.4%). After multivariable adjustment, Transfer-In patients had higher in-hospital mortality and discharge modified Rankin scale. Conclusions There is significant regional variability in the transfer of patients with AIS. Because Transfer-In patients seem to have worse short-term outcomes, these patients have the potential to negatively influence institutional mortality rates and should be accounted for explicitly in hospital risk-profiling measures.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".