Prediction of Early Arterial Recanalization and Tissue Fate in the Selection of Patients With the Greatest Potential to Benefit From Intravenous Tissue-Type Plasminogen Activator
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our objective is to determine the performance of the combination of likelihood of arterial recanalization and tissue fate to predict functional clinical outcome in patients with acute stroke. METHODS: Clinical, imaging, and outcome data were collected in 173 patients with acute ischemic stroke who presented within 4.5 hours from symptom onset, in the time window eligible for intravenous tissue-type plasminogen activator. Imaging data included Alberta Score Program Early Computed Tomographic Score (ASPECTS), site of occlusion, volume of ischemic core and penumbra, and recanalization. Outcome data consisted of modified Rankin Scale score at 90 days. We classified patients based on their baseline imaging characteristics and treatment with intravenous tissue-type plasminogen activator (yes/no) according to 5 different hypothetical prognostic algorithms: (1) based on whether patients received intravenous tissue-type plasminogen activator, (2) based on ASPECTS, (3) based on the site of occlusion, (4) based on volume of ischemic core and penumbra, and (5) based on a matrix of predicted recanalization and volume of ischemic core and penumbra. We compared the performance of such algorithms to predict good clinical outcome, defined as modified Rankin Scale score of ≤2 at 90 days. RESULTS: One hundred and twenty-four patients received intravenous tissue-type plasminogen activator; 49 did not. In the group that was treated, 46 (37%) had good outcome as opposed to 38.7% in the nontreated. The algorithm that combined the prediction of recanalization with the volume of ischemic core and penumbra showed the highest accuracy to predict good outcome (77.7%) as opposed to others (range, 43.9%-57.2%) CONCLUSIONS: The combination of predicted recanalization and tissue fate proved superior to prognosticate good clinical outcome when compared with other usual predictors.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".