Modeling the Decay in Probability of Receiving Endovascular Thrombectomy on the Basis of Time From Stroke Onset
Bibliographic record
Abstract
Background: American Heart Association guidelines specify infarct core volume as 1 determinant of eligibility for endovascular thrombectomy. 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 patient with suspected large-vessel occlusion would qualify for thrombectomy on the basis of their expected time from stroke onset to treatment can help inform the optimal prehospital emergency transport protocols, maximizing the likelihood of an excellent patient outcome. Methods: We extended a published physiological model of infarct core growth to derive a decay curve of thrombectomy eligibility (based on a given infarct core volume threshold) as a function of time from stroke onset. We then adapted an existing model of the time-dependent probability of an excellent outcome to incorporate this decay curve. Using the adapted model, we determined the optimal prehospital emergency transport protocols in Alberta, Canada, and compared these with the protocols that assumed all patients were thrombectomy eligible. Results: The probability of qualifying for thrombectomy decays exponentially as time elapses from stroke onset. We found that the area where mothership is the optimal transport protocol increased by 18.6% after incorporating our decay curve of thrombectomy eligibility into the underlying optimization model. The benefit of mothership versus drip-and-ship also increased in the areas where mothership was favored, and in areas where drip-and-ship was favored, the benefit of drip-and-ship weakened. We also performed a number of sensitivity analyses to observe how these results change on the basis of our assumptions for model parameters. Conclusion: This methodology provides a novel, physiology-based approach to derive a thrombectomy eligibility curve. These models are necessary to better optimize prehospital transport decisions and consequently improve outcomes of patients with suspected large-vessel occlusion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".