Can micro-guidewire advancement forces predict clot consistency and location to assist the first-line technique for mechanical thrombectomy?
Bibliographic record
Abstract
BACKGROUND: The identification of specific clot characteristics before mechanical thrombectomy (MTB) might allow the selection of the most effective first-line technique, thus potentially improving the procedural outcome. We aimed to evaluate if the microwire push forces could extrapolate information on clot consistency and extension before MTB, based on clot mechanical properties. METHODS: We measured in vitro the forces exerted on the proximal extremity of the guidewire during the advancement and retrieval of the guidewire through clot analogs of different compositions. In addition, we analyzed the forces exerted on the guidewire to extrapolate information about the location of the proximal and distal extremities of the clot analogs. RESULTS: The maximum forces recorded during the whole penetration phase were significantly different for hard and soft clots (median values, 55.6 mN vs 15.4 mN, respectively; P<0.0001). The maximum slope of the force curves recorded during the advancement of the guidewire for the first 3 s of penetration also significantly differentiated soft from hard clot analogs (7.6 mN/s vs 23.9 mN/s, respectively; P<0.0001). In addition, the qualitative analysis of the shape of the force curves obtained during the advancement and retrieval of the guidewire showed a good potential for the identification of the proximal and distal edges of the clot analogs. CONCLUSION: Our results demonstrated that it was possible to differentiate between soft and hard clot analogs. Furthermore, force measurements could give important information about the location of the clot extremities. Such an approach might support the selection of the first-line MTB technique, with the potential to improve the outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".