Abstract TMP18: Real-World Challenges in US Thrombectomy Transfers: Difficulty Finding an Accepting Hospital, Prolonged Travel, and Differing Perceptions of Transfer Requirements
Bibliographic record
Abstract
Introduction: Transfers can improve access to endovascular thrombectomy (ET). However, benefit is highly time-dependent and transferred patients experience poorer outcomes. We assessed perceptions of transfers processes between sending and receiving hospitals. Methods: We utilized an ongoing 2023 nationwide US electronic survey of hospitals that treat stroke patients with revascularization therapy and have publicly available contact information. Respondents were stroke directors or coordinators. Survey items analyzed included questions on certification status, transfer processes, volumes, times, delays, and requirements for transfers. We performed cross-sectional analyses of hospitals that send/receive ET transfers. We used descriptive statistics and chi-squared tests to compare sending and receiving cohorts. Results: Of 144 responding hospitals at the time of analysis, 70.1% (n=101) receive and 29.9% (n=43) send ET transfers. Most (76.2%) receiving hospitals are comprehensive stroke centers and most (95.3%) sending hospitals primary stroke centers (p<0.05). Only 39.5% of sending hospitals send ≥20 transfers annually vs. 64.4% of receiving hospitals that receive ≥20. More than 1/2 send/receive transfers to/from facilities >60 miles away, and 50% pass a closer capable hospital en route. Average door-in-door out time is >90 minutes for 51.2% of sending hospitals, and 35.0% spend >50% of this time on non-care coordination. Concerningly, 34.9% of sending hospitals are sometimes/always unable to find an accepting hospital. Perceptions of requirements for transfer vary between receiving and sending hospitals: 30.7% vs. 76.7% require large vessel occlusion confirmation, 1% vs. 14% perfusion imaging and 14.9% vs. 32.6% an available bed prior to transfer (p<0.05). Conclusion: In this real-world sample, transfers are common, perceptions of requirements for transfer differ, and there are actionable delays. Concerningly, sending hospitals are often unable to find an accepting hospital and closer hospitals are frequently passed en route. Future interventions could standardize and provide oversight of regional stroke systems of care.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".