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 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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".