Outcomes and Issues of ‘Drip and Go’ as an Inter-Hospital Cooperation System in Mechanical Thrombectomy for Acute Ischemic Stroke
Bibliographic record
Abstract
Objective: Mechanical thrombectomy in acute ischemic stroke (AIS) has become popular in recent years. Our affiliated institutes without neuro-endovascular specialists call our department to come to assist and perform thrombectomy (Drip and Go). In this study, the effectiveness of this inter-hospital cooperative system was evaluated. Methods: Between January 2016 and December 2018, "Drip and Go" was performed in a total of 29 patients (20 males, average age of 75 years) from four hospitals located within a 1-hour drive, that frequently called for AIS assistance. The background and outcomes of such cases were then retrospectively collected and evaluated. Results: The median National Institutes of Health Stroke Scale (NIHSS) and diffusion-weighed image-Alberta Stroke Programme Early CT Score (DWI-ASPECTS) were 19 and 7, respectively. Gro in puncture was performed in 27 patients (93%) within 6 h of onset. Good reperfusion (thrombolysis in cerebral infarction [TICI] 2b/3) was obtained in 24 patients (82%) with only one patient exhibiting hemorrhagic complication. A total of 12 patients (41%) had a modified Rankin Scale (mRS) score of 0-3 after 90 days or at the time of discharge. Univariate analysis identified a DWI-ASPECTS of 7 or higher as the only significant factor associated with a good neurological prognosis (P <0.05). Neurological prognosis was the most favorable at the furthest hospital where patients had a good DWI-ASPECTS. Conclusion: By employing a 1-hour arrival time window and proper patient selection, the "Drip and Go" inter-hospital cooperative system can be an alternative approach for covering areas where no neuro-endovascular specialists are available for AIS.
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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 | 0.000 |
| 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".