Characterization of Barriers to Mechanical Thrombectomy Access in Georgia
Bibliographic record
Abstract
BACKGROUND: Similar to many low- and middle-income countries, the barriers limiting wider mechanical thrombectomy (MT) access in Georgia are largely unknown. Recently, the MT access score (MTAS) was introduced as a new tool for identifying and characterizing barriers to MT access. This study aimed to implement the MTAS in Georgia, a middle-income country in Eastern Europe, to assess and characterize national barriers to MT. METHODS: We applied the MTAS, which comprises 12 weighted attributes, each scored on a 0-3 scale, resulting in a total score range of 0-36, 0 being the worst possible score. Eight members of the Mission Thrombectomy regional committee from different regions of Georgia were invited as panelists in this survey. The results of the survey are shown as a median with an interquartile range. RESULTS: The median MTAS for Georgia was 17. The lowest median scores were documented for 2 attributes: lack of prehospital large vessel occlusion-specific screening [0.0 (0.0-0.0)] and telestroke networks [0.0 (0.0-0.0)], with 87.5% of panelists assessing the score as 0 for both attributes. The highest scores were obtained for emergency medical services use [3.0 (2.0-3.0)], availability of MT operators [2.0 (2.0-2.5)] followed by MT device availability and government/insurance coverage [2.0 (2.0-2.0) for each]. CONCLUSION: MTAS is a valid tool for quantitatively assessing barriers to MT in Georgia. It identified a lack of information and the presence of physical barriers as major challenges. These findings underscore the need for targeted interventions through national stroke public health initiatives to improve access to MT.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".