Abstract HUP4: Svin Mt2020 + Global Mechanical Thrombectomy Access Barrier Score
Bibliographic record
Abstract
Introduction: Mechanical thrombectomy (MT) is a highly safe and effective standard of care for acute ischemic strokes with large vessel occlusion. However, timely access to MT is extremely limited on a global scale, with vast disparities in access between countries. MT2020+, a global non-profit initiative of SVIN, aimed to create a semi-quantitative global MT access barrier score (MTABS) to objectively measure the barriers impeding the implementation of treatment worldwide. Methods: Four independent investigators performed an in-depth systematic literature review using the peer review of electronic search strategies. Barriers to access were identified and categorized into 3 groups: information and diagnostic, physical, and financial. An international expert panel was created and scored each attribute using a modified Delphi process with the assistance of consultants from the University of Calgary W21C. A 1-9-point scale was used, with 1 being not at all important and 9 being extremely important. A meeting was held for the attributes that require deliberation. After an agreement, a list of attributes for access was elaborated. Next, a ranking of importance and individual weighting was done. We assigned a presence of or lack of an attribute a numerical value (1 for yes, 0 for no) and multiply by its weight to determine a final score. Results: After an initial screening of 2864 abstracts, 121 studies were included in the final systematic review. A total of 34 possible attributes that are barriers to access were identified. After the modified Delphi process, 26 individual attributes were selected. The MTABS was made with possible results from 0-62 points, with higher scores meaning higher barriers to access to MT. Conclusions: The global MTABS represents the first tool to quantify barriers to MT access worldwide. Its implementation will objectively measure the magnitude and identify key barriers to guide regional public health interventions to improve MT workflow and access.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".