Abstract WMP2: Outcomes of Endovascular Therapy Following Interhospital Transfer
Bibliographic record
Abstract
Purpose: Interhospital transfer for endovascular therapy (EVT) is increasingly frequent. We sought to compare the patient characteristics and in-hospital outcomes of EVT by arrival groups (transfer-in vs. front-door). Methods: We analyzed data from 22,881 acute ischemic stroke (AIS) patients treated with EVT in Get With The Guideline (GWTG)-Stroke from January 2012 through September 2016 at 553 hospitals. Patient level characteristics were compared by arrival groups. Multivariable logistic regression models were generated to examine the association between arrival mode and in-hospital outcomes. Results: Of 22,881 patients receiving EVT 41.5% (9,510) arrived to the EVT providing hospital after interhospital transfer. Transfer-in patients were more likely to be younger and of white race, with arrival during off-hours and lower initial recorded blood pressure. Transfer-in patients had significantly longer last known well to EVT initiation time (296 min vs 224 min, absolute standardized difference 67.09) but were more likely to have door to EVT initiation time of ≤ 90 minutes (Table 1). In-hospital outcomes were worse for transfer-in EVT patients in unadjusted analyses but in the risk adjusted models, there was no difference in the rates of in-hospital mortality and symptomatic intracranial hemorrhage. After risk adjustment, transfer-in patients were still less likely to have independent ambulation at discharge and discharged to home (Table 1). Conclusions: Interhospital transfer of AIS patients for endovascular therapy is associated with significant delay to treatment. In spite of similar risk adjusted in-hospital mortality and symptomatic intracerebral hemorrhage rates, transfer-in patients are less likely to have favorable discharge functional status.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".