Abstract WP43: Modeling The Decay In Probability Of Receiving Endovascular Thrombectomy Based On Time From Stroke Onset
Bibliographic record
Abstract
Introduction: Large-vessel occlusion (LVO) stroke patients with infarct core volumes greater than 70 mL do not meet current AHA guidelines for thrombectomy eligibility. Therefore, it is important to understand how time-dependent infarct core growth translates to a patient’s declining probability of thrombectomy eligibility. Modeling the probability that a suspected LVO patient would qualify for thrombectomy can help inform the optimal prehospital emergency transport strategy which maximizes the likelihood of an excellent patient outcome. Methods: We determined the time from stroke onset required for an infarct core to grow to 70 mL as a function of penumbra volume and collateral score. After applying a series of numerical interpolations, our framework outputted the probability a suspected LVO patient qualifies for thrombectomy (based on core volume threshold) as a function of time from stroke onset to imaging. We then incorporated this function into an existing model of prehospital emergency transport, which previously assumed all patients were thrombectomy-eligible, in order to assess the impact that considering treatment eligibility had on optimal transport decisions. Results: An exponential decay curve best fit the numerically-generated probability function. We found that after integrating this eligibility model into the emergency transport decision tool the optimal transport strategy changed from drip and ship to mothership in 8.5% of possible patient pickup locations in Alberta, Canada. Conclusions: This methodology provides a novel, mechanistic approach to deriving a thrombectomy eligibility curve. As a result, we are able to better optimize prehospital transport decisions to improve outcomes of suspected LVO patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".