Mechanical Thrombectomy for Acute Stroke With the Alligator Retrieval Device
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recanalization of occluded vessels in acute ischemic stroke is associated with improved outcome. Devices that can quickly and safely remove thrombus and promote recanalization are useful in the management of these patients. The Alligator retrieval device, developed for endovascular foreign body retrieval, may also be useful for thrombus removal. METHODS: Seven patients with acute ischemic stroke (aged 31 to 88 years) who underwent intra-arterial therapy with the Alligator retrieval device at our center are presented. RESULTS: The Alligator retrieval device was able to retrieve the thrombus in 5 of 7 cases with good to excellent recanalization seen and was unsuccessful in 2 of 7 patients. Complete recanalization was obtained in one of 7 patients and near complete recanalization obtained in 4 of 7 patients. Three of the 7 patients had good outcome at 3 months and 3 of 7 patients died within 30 days of treatment. CONCLUSIONS: The Alligator retrieval device was successfully able to remove thrombus in the majority of cases. It appears to have increased success in proximal occlusions in relatively straight segments. In properly selected cases, it may be a useful device in intra-arterial stroke management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".