"Thrombectomy and Back:" A Novel Approach for Treating Patients with Large Vessel Occlusion in the Eastern Province of Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Timely access to comprehensive stroke centers for patients suffering from acute ischemic stroke due to large vessel occlusion (LVO) remains a commonly encountered obstacle worldwide, especially in areas with no comprehensive stroke or thrombectomy-capable stroke centers. OBJECTIVE: To present our novel experience with a "thrombectomy-and-back" model implemented in the Eastern Province of Saudi Arabia. METHODS: King Fahd Hospital of the University (KFHU), a 600-bed hospital located in Al Khobar with an open-access emergency department, was designated as a comprehensive stroke center in the Eastern Province. "Thrombectomy-and-back" was designed such that the neurologist in the referring hospital directly communicates with the attending neurovascular team at KFHU for their anticipation of the case, and subsequently confirms LVO presence through urgent acquisition of a CT and a CT angiogram. Once LVO was confirmed, the patients were timely transferred to KFHU for mechanical thrombectomy. Upon procedure completion, the patients returned to the referring hospital with the same medical and EMS team. The safety of transfer and peri-procedural complications were analyzed. RESULTS: From December 2017 to December 2019, 20 thrombectomy-and-back codes were activated, of which 10 were deactivated on negative LVO and 10 remained activated. Of these 10 patients, 2 required admission to our hospital's Neuro-ICU: one was because the middle cerebral artery reoccluded during the procedure and the other was due to hemodynamic instability upon arrival; this first patient passed away 2 months later due to the complications of the malignant left middle cerebral artery stroke. CONCLUSIONS: The novel Thrombectomy-and-Back model in the Eastern Province of Saudi Arabia has proved to be a safe and efficient approach for patients presenting with LVO to receive timely interventional therapy and minimizing futile transfers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".