Mechanical thrombectomy with the Solitaire stent: is there a learning curve in achieving rapid recanalization times?
Bibliographic record
Abstract
METHODS: In acute ischemic stroke, good outcome following successful recanalization is time dependent. In patients undergoing endovascular therapy at our institution, recanalization times with the Solitaire stent were retrospectively evaluated to assess for the presence of a learning curve in achieving rapid recanalization. METHODS: We reviewed patients who presented to our stroke center and achieved successful recanalization with the Solitaire stent exclusively. Time intervals were calculated (CT to angiography arrival, angiography arrival to groin puncture, groin puncture to first deployment, and deployment to recanalization) from time stamped images and angiography records. Patients were divided into three sequential groups, with overall CT to recanalization time and subdivided time intervals compared. RESULTS: 83 patients were treated with the Solitaire stent from May 2009 to February 2012. Recanalization (Thrombolyis in Cerebral Infarction score 2A) occurred in 75 (90.4%) patients. CT to recanalization demonstrated significant improvement over time, which was greatest between the first 25 and the most recent 25 cases (161-94 min; p<0.01). The maximal contribution to this was from improvements in first stent deployment to recanalization time (p=0.001 between the first and third groups), with modest contributions from moving patients from CT to the angiography suite faster (p=0.02 between the first and third groups) and from groin puncture to first stent deployment (p=0.02 between the first and third groups). CONCLUSIONS: There is a learning curve involved in the efficient use of the Solitaire stent in endovascular acute stroke therapy. Along with improvements in patient transfer to angiography and improved efficiency with intracranial access, mastering this device contributed significantly towards reducing recanalization times.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".