14th World Stroke Congress, Singapore, 26-29 October 2022
Bibliographic record
Abstract
Background and Aims: Access to emergent mechanical thrombectomy (MT) for acute ischemic stroke with large vessel occlusions is limited worldwide with vast disparities between countries. MT2020+, a global initiative of SVIN, aimed to create a global MT access score using systematic, mixed methods approach performed to objectively measure the drivers of access needed to accelerate treatment worldwide. Methods: Four independent investigators performed an in-depth systematic literature review using the Peer Review of Electronic Search Strategies. Access drivers were identified and categorized into 3 groups: information and diagnostic access, physical access and financial access. A multispecialty international expert panel was created and scored each attribute using a modified Delphi process with assistance of University of Calgary W21C. A 1-9-point scale was used, with 1 being not at all important and 9 being extremely important, followed by virtual face to face meeting to deliberate attributes whose mean fell between 4-6. Results: After initial screening of 2864 abstracts, 523 studies were included in the final systematic review. A total of 34 possible attributes that drive access were identified. After the modified Delphi process, 26 individual attributes were determined to be of significance in the creation of a MT access score. 5 attributes were related to financial access, 11 were related to physical access and 10 were information and diagnostic drivers of access. Conclusions: The global MT access score represents a tool to evaluate MT access barriers in different world regions. Weighting of the individual attributes and validation of the score will be needed prior to its implementation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.183 | 0.100 |
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".