Abstract WP52: There is No Association Between the Number of Stent Retriever Passes & the Incidence of Hemorrhagic Transformation for Patients Undergoing Mechanical Thrombectomy
Bibliographic record
Abstract
Background: Previous research has primarily investigated the association between hemorrhagic transformation (HT) incidence and baseline variables (i.e. pre-procedural variables) rather than the association between HT incidence and endovascular treatment (EVT) procedural variables (e.g. the number of passes with a stent retriever). Current stent retriever guidelines recommend a maximum of 2 passes per device and not more than 3 passes per vessel. Objective: To assess the association, if any, that exists between the number of passes with a stent retriever and the incidence of HT for patients undergoing mechanical thrombectomy. Materials and Methods: Data from patients who underwent EVT with a Trevo, Solitaire, or Penumbra stent retriever from the years 2012 to 2018 was collected and categorized according to the incidence of HT. HT was defined as any intracranial hemorrhage in the territory of the initial ischemic event within admission and determined via CT scan read by radiology. Univariate and bivariate statistical analyses were conducted on the number of passes per procedure with a stent retriever, patient demographic data, patient morbidities, and patient outcomes to investigate their association with HT incidence. Results: Of 329 total patients, 46 (14%) had HT. The HT group had an average[SD] of 1.65[0.67] and range of [1-3] passes while the non-HT group had an average[SD] of 1.63[0.86] and range of [1-5] passes per procedure. Admission NIHSS score (p = 0.0003) and the incidence of diabetes mellitus (DM) (p=0.05) were significantly higher in the HT group. Upon bivariate logistic regression, the number of passes failed to show any association with HT (p = 0.804) while admission NIHSS score was found to have an OR of 1.07 (95% CI: 1.03 - 1.12, p = 0.001) with HT incidence. Conclusion: No significant association was found between HT incidence and the number of passes with a stent retriever. Further research investigating additional EVT procedural variables is warranted.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".