Mechanical Thrombectomy Workshops Improve Procedural Knowledge and Skills Among Neurointerventional Teams in Low- to Middle-Income Countries
Bibliographic record
Abstract
BACKGROUND: While mechanical thrombectomy (MT) is proven to be lifesaving and disability sparing, there remains a disparity in its access in low- to middle-income countries. We hypothesized that team-based MT workshops would improve MT knowledge and skills. METHODS: We designed a 22-hour MT workshop, conducted as 2 identical events: in English (Jamaica, January 2022) and in Spanish (Dominican Republic, May 2022). The workshops included participating neurointerventional teams (practicing neurointerventionalists, neurointerventional nurses, and technicians) focused on acute stroke due to large vessel occlusion. The course faculty led didactic and hands-on components, covering topics from case selection and postoperative management to device technology and MT surgical techniques. Attendees were evaluated on stroke knowledge and MT skills before and after the course using a multiple choice exam and simulated procedures utilizing flow models under fluoroscopy, respectively. Press conferences for public education with invited government officials were included to raise stroke awareness. RESULTS: Twenty-two physicians and their teams from 8 countries across the Caribbean completed the didactic and hands-on training. Overall test scores (n=18) improved from 67% to 85% ( P <0.002). Precourse and postcourse hands-on assessments demonstrated reduced time to completion from 36.5 to 21.1 minutes ( P <0.001). All teams showed an improvement in measures of good MT techniques, with 39% improvement in complete reperfusion. Eight teams achieved a Thrombolysis in Cerebral Infarction score of 3 on pre-course versus 15 of 18 teams on post-course. There was a significant reduction in total potentially dangerous maneuvers (70% pre versus 20% post; P <0.002). Universally, the workshop was rated as satisfactory and likely to change practice in 93% Dominican Republic and 75% Jamaica. CONCLUSIONS: A team-based hands-on simulation approach to MT training is novel, feasible, and effective in improving procedural skills. Participants viewed these workshops as practice-changing and instrumental in creating a pathway for increasing access to MT in low- to middle-income countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".