E.5 Designing a paradigm for post endovascular therapy imaging
Bibliographic record
Abstract
Background: There is no guideline for imaging post endovascular therapy (EVT). MRI is considered superior to noncontrast CT for assessment of final infarct volume and to distinguish contrast from hemorrhage. We sought to align the post EVT imaging practices with those after intravenous thrombolysis Methods: We reviewed the EMR records for all EVT patients from Jan 1, 2019 to Dec 31, 2021. We assessed quantity of CT within 24h of EVT, quantity of MRIs performed, and indications listed. We then undertook an educational program targeting stakeholders. The objective was to transition to MRI at 24h for imaging post EVT. Exceptions included neurologic change, need for antiplatelet infusion, or intraoperative complications. Results: Post intervention, a significant reduction in CT within 24h (-28%, P=0.01) and increase in MRIs (+42%, P<0.00). CT within 24h per patient dropped by 50% (1.12 pre vs 0.57 post). Radiation dose per patient dropped by 49%. Average imaging costs increased by 17%, and the number of transfers off unit for imaging increased by 11%. Good functional outcome dropped from 44% preintervention to 34% postintervention (P=0.06). Conclusions: This represents the first systematic evaluation of post EVT imaging in a single center. We demonstrate successful behavior changes for post EVT imaging.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".