Time of day and endovascular treatment decision in acute stroke with relative endovascular treatment indication: insights from UNMASK EVT international survey
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The decision to proceed with endovascular thrombectomy should ideally be made independent of inconvenience factors, such as daytime. We assessed the influence of patient presentation time on endovascular therapy decision making under current local resources and assumed ideal conditions in acute ischemic stroke with level 2B evidence for endovascular treatment. METHODS AND MATERIALS: In an international cross sectional survey, 607 stroke physicians from 38 countries were asked to give their treatment decisions to 10 out of 22 randomly assigned case scenarios. Eleven scenarios had level 2B evidence for endovascular treatment: 7 daytime scenarios (7:00 am-5:00 pm) and four night time cases (5:01 pm- 6:59 am). Participants provided their treatment approach assuming (A) there were no practice constraints and (B) under their current local resources. Endovascular treatment decisions in the 11 scenarios were analyzed according to presentation time with adjustment for patient and physician characteristics. RESULTS: Participants selected endovascular therapy in 74.2% under assumed ideal conditions, and 70.7% under their current local resources of night time scenarios, and in 67.2% and 63.8% of daytime scenarios. Night time presentation did not increase the probability of a treatment decision against endovascular therapy under current local resources or assumed ideal conditions. CONCLUSION: Presentation time did not influence endovascular treatment decision making in stroke patients in this international survey.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".