P.046 Impact of telemedicine evaluation among ischemic stroke patients transferred for endovascular thrombectomy: data from the OPTIMISE registry
Bibliographic record
Abstract
Background: Telemedicine evaluation for treatment of acute stroke patients with IV thrombolysis has been shown to be beneficial. Its usefulness for the evaluation of patients transferred from a primary stroke centre (PSC) to a comprehensive stroke centre (CSC) for endovascular thrombectomy (EVT) is less well defined. Methods: We retrospectively analyzed the Canadian OPTIMISE registry which included data from 20 comprehensive stroke centers across Canada between January 1, 2018, and December 31, 2022 to compare treatment metrics and early outcomes between two groups: patients evaluated by telemedicine (TM) and patients evaluated in person (non-TM) at the PSC prior to CSC transfer. Results: We included 3317 patients who were transferred from a PSC to a CSC for: 888 TM and 2429 non-TM. There were no major differences in baseline characteristics, including intravenous thrombolysis administration, though the TM group included more men. TM patients had longer onset-to-puncture times (441 vs 403 minutes, p<0.001) and higher symptomatic intracerebral hemorrhage (sICH) rates (7.4% vs 3.7%, p<0.001), but CSC door-to-puncture times and successful recanalization rates did not differ. Conclusions: Patients transferred to a CSC for EVT first evaluated by TM had similar characteristics to those evaluated in person at the PSC, but longer onset-to-puncture times and higher sICH rates.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".