Abstract WMP32: Variation in the Rates of Acute Ischemic Stroke Transfers Between Hospitals in the United States
Bibliographic record
Abstract
Background: Many patients are transferred from emergency departments or inpatient units to stroke centers for advanced acute ischemic stroke (AIS) care, especially after intravenous tissue plasminogen activator (tPA). We sought to determine variation in the rates of AIS patient transfer in the US. Methods: Using data from the national Get With The Guidelines-Stroke registry, we analyzed AIS cases from 01/2010 to 03/14. Transfer-in was defined as transfer of AIS patients from other hospitals. Due to large sample size, instead of p-values, standardized differences were reported and a map of transfer-in rates across the US constructed. Results: Of the 970,390 patients discharged from 1,646 hospitals in the US, 87% were admitted via the ER or direct admission (front door) vs. 13% transferred-in. While most hospitals (61%) had transfer-in rates of < 5% of all AIS patients, a minority (17%) had high (>15%) transfer-in rates. High transfer-in hospitals were more often in the Midwest, were larger, and had higher annual AIS and IV tPA case volumes, and were also more often teaching hospitals and stroke centers (primary or comprehensive) (Table and Figure).. IV tPA was used more frequently in eligible patients in high-volume transfer-in hospitals (Table); otherwise, stroke quality of care was similar. Conclusions: There is significant regional- and state-level variability in the transfer of AIS patients. This may reflect differences in resource availability and the distribution of smaller, under-resourced hospitals that frequently transfer patients for advanced care after stabilization. Additional research is warranted to understand this variation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".