Abstract TP147: Investigating Potential Racial And Ethnic Biases In Mechanical Thrombectomy Selection For Expanded Indication Populations
Bibliographic record
Abstract
Introduction: Endovascular thrombectomy (EVT) performance in clinical practice has grown substantially, including in patient groups previously excluded from clinical trials such as those with advanced age, lower Alberta Stroke Program Early CT Score (ASPECTS) and distal occlusions. However, the access and utilization of EVT in these non-randomized controlled trial (RCT) supported populations across different racial and ethnic groups remains incompletely characterized Methods: Using our prospectively maintained multi-center registry which includes four comprehensive stroke centers around the greater Houston area, we identified patients with large vessel occlusion (LVO) acute ischemic stroke (AIS). Patients were included if LVO was confirmed by non-invasive vascular imaging. The primary outcome was rate of performance of EVT in patients with low ASPECTS (0-5), advanced age (> 80), and distal occlusion (M2 and beyond) by race and was determined by Fisher’s exact test. Results: Among 2,614 patients with LVO AIS, 1,239 (47%) were female, median NIHSS was 13 [interquartile range 5-20], ASPECTS was 9 [7-10]. 28% of the cohort was black, 52% white and 4% Hispanic. The most common occlusion locations were M1 MCA (34%) and M2 MCA (19%). Among the 538 patients aged > 80 years old, 232 (43%) underwent EVT. There were no significant differences in the proportions of patients treated by race (43% white vs 43% black, p=1) . Among 175 patients with ASPECTS 0-5, 59 (34%) underwent EVT. There were no significant differences in the proportions of patients treated by race (44% white vs 35% black, p=0.37). Among 760 patients with occlusion of M2 and beyond, 224 (30%) underwent EVT. There were no significant differences in the proportions of patients treated by race (33% white, 29% black, p=0.28). Conclusion: In this registry from a diverse urban population, we did not identify disparities in EVT performance in patients with non-RCT supported indications by race. Further studies in other clinical settings will be important.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".